Publications

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[Journal Articles] [Book Chapters] [Conference Articles] [ArXiv & Technical Reports] [PhD Theses] [Master’s Theses]

Journal Articles

  1. A. Kitanov and V. Indelman, “Topological Belief Space Planning for Active SLAM with Pairwise Gaussian Potentials and Performance Guarantees,” International Journal of Robotics Research (IJRR), no. 1, 2024.
    Kitanov24ijrr.pdf DOI: 10.1177/02783649231204898
  2. A. Zhitnikov and V. Indelman, “Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints,” IEEE Transactions on Robotics (T-RO), 2024.
    Zhitnikov24tro.pdf DOI: 10.1109/TRO.2023.3341625
  3. M. Barenboim, M. Shienman, and V. Indelman, “Monte Carlo Planning in Hybrid Belief POMDPs,” IEEE Robotics and Automation Letters (RA-L), no. 8, Aug. 2023.
    Barenboim23ral.pdf DOI: 10.1109/LRA.2023.3282773 Barenboim23ral.supplementary
  4. J. Placed et al., “A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers,” IEEE Transactions on Robotics (T-RO), no. 3, Jun. 2023.
    Placed23tro.pdf DOI: 10.1109/TRO.2023.3248510
  5. V. Tchuiev and V. Indelman, “Epistemic Uncertainty Aware Semantic Localization and Mapping for Inference and Belief Space Planning,” Artificial Intelligence, Special Issue on Risk-Aware Autonomous Systems, 2023.
    Tchuiev23ai.pdf DOI: 10.1016/j.artint.2023.103903
  6. M. Barenboim, I. Lev-Yehudi, and V. Indelman, “Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees,” IEEE Robotics and Automation Letters (RA-L), no. 10, Oct. 2023.
    Barenboim23ral2.pdf DOI: 10.1109/LRA.2023.3311205 Barenboim23ral2.supplementary
  7. O. Shelly and V. Indelman, “Hypotheses Disambiguation in Retrospective,” IEEE Robotics and Automation Letters (RA-L), no. 2, Apr. 2022.
    Shelly22ral.pdf DOI: 10.1109/LRA.2022.3143298 Shelly22ral.supplementary Shelly22ral.poster
  8. I. Zilberman, E. Rivlin, and V. Indelman, “Incorporating Compositions in Qualitative Approaches,” IEEE Robotics and Automation Letters (RA-L), no. 2, Apr. 2022.
    Zilberman22ral.pdf DOI: 10.1109/LRA.2022.3144525 Zilberman22ral.poster
  9. E. Farhi and V. Indelman, “Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm,” Autonomous Robots, Aug. 2022.
    Farhi22arj.pdf DOI: 10.1007/s10514-022-10045-w
  10. K. Elimelech and V. Indelman, “Simplified decision making in the belief space using belief sparsification,” International Journal of Robotics Research (IJRR), no. 5, Jun. 2022.
    Elimelech22ijrr.pdf DOI: 10.1177/02783649221076381
  11. A. Zhitnikov and V. Indelman, “Simplified Risk Aware Decision Making with Belief Dependent Rewards in Partially Observable Domains,” Artificial Intelligence, Special Issue on “Risk-Aware Autonomous Systems: Theory and Practice", Aug. 2022.
    Zhitnikov22ai.pdf DOI: 10.1016/j.artint.2022.103775
  12. M. Shienman, A. Kitanov, and V. Indelman, “FT-BSP: Focused Topological Belief Space Planning,” IEEE Robotics and Automation Letters (RA-L), no. 3, Jul. 2021.
    Shienman21ral.pdf Shienman21ral.slides DOI: 10.1109/LRA.2021.3068947 Shienman21ral.video
  13. K. Elimelech and V. Indelman, “Efficient Modification of the Upper Triangular Square Root Matrix on Variable Reordering,” IEEE Robotics and Automation Letters (RA-L), no. 2, Apr. 2021.
    Elimelech21ral.pdf Elimelech21ral.slides DOI: 10.1109/LRA.2020.3048663 Elimelech21ral.video
  14. V. Tchuiev and V. Indelman, “Distributed Consistent Multi-Robot Semantic Localization and Mapping,” IEEE Robotics and Automation Letters (RA-L), no. 3, Jul. 2020.
    Tchuiev20ral.pdf DOI: 10.1109/LRA.2020.3003275 Tchuiev20ral.supplementary Tchuiev20ral.video
  15. Y. Feldman and V. Indelman, “Spatially-Dependent Bayesian Semantic Perception under Model and Localization Uncertainty,” Autonomous Robots, 2020.
    Feldman20arj.pdf DOI: 10.1007/s10514-020-09921-0
  16. D. Kopitkov and V. Indelman, “General Purpose Incremental Covariance Update and Efficient Belief Space Planning via Factor-Graph Propagation Action Tree,” International Journal of Robotics Research (IJRR), no. 14, Sep. 2019.
    Kopitkov19ijrr.pdf URL: https://journals.sagepub.com/doi/10.1177/0278364919875199
  17. T. Regev and V. Indelman, “Decentralized Multi-Robot Belief Space Planning in Unknown Environments via Identification and Efficient Re-Evaluation of Impacted Paths,” Autonomous Robots, Special Issue on Online Decision Making in Multi-Robot Coordination, no. 4, 2018.
    Regev17arj.pdf DOI: 10.1007/s10514-017-9659-4
  18. M. Chojnacki and V. Indelman, “Vision-based Dynamic Target Trajectory and Ego-motion Estimation Using Incremental Light Bundle Adjustment,” International Journal of Micro Air Vehicles, Special Issue on Estimation and Control for MAV Navigation in GPS-denied Cluttered Environments, no. 2, 2018.
    Chojnacki18mav.pdf
  19. V. Ovechkin and V. Indelman, “BAFS: Bundle Adjustment with Feature Scale Constraints for Enhanced Estimation Accuracy,” IEEE Robotics and Automation Letters (RA-L), no. 2, 2018.
    Ovechkin18ral.pdf DOI: 10.1109/LRA.2018.2792141 Ovechkin18ral.poster
  20. S. Pathak, A. Thomas, and V. Indelman, “A Unified Framework for Data Association Aware Belief Space Planning and Perception,” International Journal of Robotics Research (IJRR), no. 2-3, 2018.
    Pathak18ijrr.pdf DOI: 10.1177/0278364918759606
  21. V. Tchuiev and V. Indelman, “Inference over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception,” IEEE Robotics and Automation Letters (RA-L), no. 4, 2018.
    Tchuiev18ral.pdf Tchuiev18ral.slides DOI: 10.1109/LRA.2018.2852844
  22. X. Yan, V. Indelman, and B. Boots, “Incremental Sparse GP Regression for Continuous-time Trajectory Estimation and Mapping,” Robotics and Autonomous Systems, 2017.
    Yan17ras.pdf URL: http://www.sciencedirect.com/science/article/pii/S0921889016300434
  23. V. Indelman, “Cooperative Multi-Robot Belief Space Planning for Autonomous Navigation in Unknown Environments,” Autonomous Robots, Special Issue on Active Perception, 2017.
    Indelman17arj.pdf DOI: 10.1007/s10514-017-9620-6
  24. D. Kopitkov and V. Indelman, “Computationally Efficient Belief Space Planning via Augmented Matrix Determinant Lemma and Re-Use of Calculations,” IEEE Robotics and Automation Letters (RA-L), no. 2, 2017.
    Kopitkov17ral.pdf Kopitkov17ral.slides URL: http://ieeexplore.ieee.org/document/7801141/ Kopitkov17ral.supplementary
  25. D. Kopitkov and V. Indelman, “No Belief Propagation Required: Belief Space Planning in High-Dimensional State Spaces via Factor Graphs, Matrix Determinant Lemma and Re-use of Calculation,” International Journal of Robotics Research (IJRR), no. 10, 2017.
    Kopitkov17ijrr.pdf DOI: 10.1177/0278364917721629
  26. V. Indelman, “No Correlations Involved: Decision Making Under Uncertainty in a Conservative Sparse Information Space,” IEEE Robotics and Automation Letters (RA-L), no. 1, 2016.
    Indelman16ral.pdf URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7383252 Indelman16ral.supplementary
  27. V. Indelman, E. Nelson, J. Dong, N. Michael, and F. Dellaert, “Incremental Distributed Inference from Arbitrary Poses and Unknown Data Association: Using Collaborating Robots to Establish a Common Reference,” IEEE Control Systems Magazine (CSM), Special Issue on Distributed Control and Estimation for Robotic Vehicle Networks, no. 2, 2016.
    Indelman16csm.pdf URL: http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&arnumber=7434165
  28. V. Indelman, L. Carlone, and F. Dellaert, “Planning in the Continuous Domain: a Generalized Belief Space Approach for Autonomous Navigation in Unknown Environments,” International Journal of Robotics Research (IJRR), no. 7, 2015.
    Indelman15ijrr.pdf URL: http://ijr.sagepub.com/content/34/7/849.full.pdf+html
  29. V. Indelman, R. Roberts, and F. Dellaert, “Incremental Light Bundle Adjustment for Structure From Motion and Robotics,” Robotics and Autonomous Systems, 2015.
    Indelman15ras.pdf URL: http://www.sciencedirect.com/science/article/pii/S0921889015000810
  30. S. Williams, V. Indelman, M. Kaess, R. Roberts, J. Leonard, and F. Dellaert, “Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing,” International Journal of Robotics Research (IJRR), Oct. 2014.
    Williams14ijrr.pdf URL: http://ijr.sagepub.com/content/33/12/1544
  31. V. Indelman, S. Wiliams, M. Kaess, and F. Dellaert, “Information Fusion in Navigation Systems via Factor Graph Based Incremental Smoothing,” Robotics and Autonomous Systems, no. 8, Aug. 2013.
    Indelman13ras.pdf DOI: 10.1016/j.robot.2013.05.001
  32. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Graph-Based Distributed Cooperative Navigation for a General Multi-Robot Measurement Model,” International Journal of Robotics Research (IJRR), no. 9, Aug. 2012.
    Indelman12ijrr.pdf URL: http://ijr.sagepub.com/content/31/9/1057
  33. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Distributed Vision-Aided Cooperative Localization and Navigation Based on Three-View Geometry,” Robotics and Autonomous Systems, no. 6, Jun. 2012.
    Indelman12ras.pdf URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5747546&tag=1
  34. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Real-Time Vision-Aided Localization and Navigation Based on Three-View Geometry,” IEEE Transactions on Aerospace and Electronic Systems, no. 3, Jul. 2012.
    Indelman12taes.pdf URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6237590
  35. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Navigation Aiding Based on Coupled Online Mosaicking and Camera Scanning,” AIAA Journal of Guidance, Control and Dynamics, no. 6, 2010.
    Indelman10jgcd.pdf

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Book Chapters

  1. M. Shienman and V. Indelman, “Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints,” in Robotics Research, Springer, 2023.
    Shienman23chapter.pdf DOI: 10.1007/978-3-031-25555-7_8
  2. K. Elimelech and V. Indelman, “Introducing PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning,” in Robotics Research, Springer, 2022.
    Elimelech19isrr_chapter.pdf DOI: 10.1007/978-3-030-95459-8_6
  3. K. Elimelech and V. Indelman, “Fast Action Elimination for Efficient Decision Making and Belief Space Planning Using Bounded Approximations,” in Robotics Research, Springer, 2020.
    Elimelech20chapter.pdf URL: https://link.springer.com/chapter/10.1007/978-3-030-28619-4_58
  4. V. Indelman, “Towards Cooperative Multi-Robot Belief Space Planning in Unknown Environments,” in Robotics Research, Springer, 2018.
    URL: http://www.springer.com/gp/book/9783319515311
  5. X. Yan, V. Indelman, and B. Boots, “Incremental Sparse GP Regression for Continuous-Time Trajectory Estimation and Mapping,” in Robotics Research, Springer, 2018.
    URL: http://www.springer.com/gp/book/9783319515311
  6. E. Nelson, V. Indelman, N. Michael, and F. Dellaert, “An Experimental Study of Robust Distributed Multi-Robot Data Association from Arbitrary Poses,” in Experimental Robotics, The 14th International Symposium on Experimental Robotics, Springer, 2016, pp. 323–338.
    Nelson16chapter.pdf URL: http://link.springer.com/chapter/10.1007%2F978-3-319-23778-7_22
  7. V. Indelman, L. Carlone, and F. Dellaert, “Towards Planning in Generalized Belief Space,” in Robotics Research, The 16th International Symposium ISRR, Springer, 2016, pp. 593–609.
    Indelman16chapter.pdf URL: http://link.springer.com/chapter/10.1007%2F978-3-319-28872-7_34
  8. V. Indelman and F. Dellaert, “Incremental Light Bundle Adjustment: Probabilistic Analysis and Application to Robotic Navigation,” in New Development in Robot Vision, Springer Berlin Heidelberg, 2015, pp. 111–136.
    Indelman15chapter.pdf DOI: 10.1007/978-3-662-43859-6_7

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Conference Articles

  1. I. Lev-Yehudi, M. Barenboim, and V. Indelman, “Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice,” in 38th AAAI Conference on Artificial Intelligence (AAAI-24), Feb. 2024.
    LevYehudi24aaai.pdf LevYehudi24aaai.slides LevYehudi24aaai.poster
  2. A. Zhitnikov and V. Indelman, “Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains,” in International Joint Conference on Artificial Intelligence (IJCAI), journal track, Aug. 2023.
    Zhitnikov23ijcai.pdf Zhitnikov23ijcai.poster
  3. M. Barenboim and V. Indelman, “Online POMDP Planning with Anytime Deterministic Guarantees,” in Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023.
    Barenboim23nips.pdf Barenboim23nips.supplementary Barenboim23nips.poster
  4. M. Shienman and V. Indelman, “D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints,” in IEEE International Conference on Robotics and Automation (ICRA), *Outstanding Paper Award Finalist*, May 2022.
    Shienman22icra.pdf Shienman22icra.supplementary Shienman22icra.poster
  5. M. Barenboim and V. Indelman, “Adaptive Information Belief Space Planning,” in the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI), Jul. 2022.
    Barenboim22ijcai.pdf arXiv: https://arxiv.org/pdf/2201.05673.pdf Barenboim22ijcai.supplementary
  6. I. Zilberman and V. Indelman, “Qualitative Belief Space Planning via Compositions,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2022.
    Zilberman22iros.pdf Zilberman22iros.supplementary Zilberman22iros.video
  7. O. Sztyglic and V. Indelman, “Speeding up POMDP Planning via Simplification,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2022.
    Sztyglic22iros.pdf Sztyglic22iros.supplementary Sztyglic22iros.video
  8. T. Lemberg and V. Indelman, “Hybrid Belief Pruning with Guarantees for Viewpoint-Dependent Semantic SLAM,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2022.
    Lemberg22iros.pdf Lemberg22iros.video
  9. M. Shienman and V. Indelman, “Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints,” in International Symposium on Robotics Research (ISRR), Sep. 2022.
    Shienman22isrr.pdf Shienman22isrr.supplementary
  10. Y. Feldman and V. Indelman, “Towards Self-Supervised Semantic Representation with a Viewpoint-Dependent Observation Model,” in International Conference on Computational Photography (ICCP), May 2021.
    Feldman21iccp.poster
  11. Y. Feldman and V. Indelman, “Autonomous Semantic Perception in Uncertain Environments,” in RSS Pioneers Workshop, Jul. 2021.
    Feldman21rss_ws.pdf Feldman21rss_ws.poster
  12. Y. Feldman and V. Indelman, “Towards Self-Supervised Semantic Representation with a Viewpoint-Dependent Observation Model,” in Workshop on Self-Supervised Robot Learning, in conjunction with Robotics: Science and Systems (RSS), Jul. 2020.
    Feldman20rss_ws.pdf Feldman20rss_ws.supplementary Feldman20rss_ws.video
  13. R. Mor and V. Indelman, “Probabilistic Qualitative Localization and Mapping,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020.
    Mor20iros.pdf Mor20iros.supplementary
  14. O. Asraf and V. Indelman, “Experience-Based Prediction of Unknown Environments for Enhanced Belief Space Planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020.
    Asraf20iros.pdf Asraf20iros.supplementary
  15. D. Kopitkov and V. Indelman, “Neural Spectrum Alignment: Empirical Study,” in International Conference on Artificial Neural Networks (ICANN), Sep. 2020.
    Kopitkov20icann.pdf Kopitkov20icann.supplementary
  16. E. Farhi and V. Indelman, “iX-BSP: Belief Space Planning through Incremental Expectation,” in IEEE International Conference on Robotics and Automation (ICRA), May 2019.
    Farhi19icra.pdf Farhi19icra.supplementary Farhi19icra.poster
  17. A. Kitanov and V. Indelman, “Focus on What Matters: Topological Aspects in Information-Theoretic Belief Space Planning,” in Workshop on Topological Methods in Robot Planning, in conjunction with the IEEE International Conference on Robotics and Automation (ICRA), May 2019.
    Kitanov19icra_ws.pdf Kitanov19icra_ws.slides Kitanov19icra_ws.poster
  18. K. Elimelech and V. Indelman, “PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning,” in Workshop on Toward Online Optimal Control of Dynamic Robots, in conjunction with the IEEE International Conference on Robotics and Automation (ICRA), May 2019.
    Elimelech19icra_ws.poster
  19. E. Farhi and V. Indelman, “Tear Down that Wall: Calculation Reuse Across Inference and Belief Space Planning,” in Workshop on Toward Online Optimal Control of Dynamic Robots, in conjunction with the IEEE International Conference on Robotics and Automation (ICRA), May 2019.
    Farhi19icra_ws.pdf Farhi19icra_ws.poster
  20. K. Elimelech and V. Indelman, “Introducing PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning,” in International Symposium on Robotics Research (ISRR), Oct. 2019.
    Elimelech19isrr.pdf Elimelech19isrr.slides
  21. V. Tchuiev, Y. Feldman, and V. Indelman, “Data Association Aware Semantic Mapping and Localization via a Viewpoint Dependent Classifier Model,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2019.
    Tchuiev19iros.pdf Tchuiev19iros.slides
  22. K. Elimelech and V. Indelman, “Efficient Belief Space Planning using Sparse Approximations,” in RSS Pioneers Workshop, 2019.
    Elimelech19rss_ws.pdf
  23. D. Kopitkov and V. Indelman, “Neural Spectrum and Gradient Similarity,” in DeepMath - Conference on the Mathematical Theory of Deep Neural Networks, Nov. 2019.
    Kopitkov19deepmath.poster
  24. Y. Feldman and V. Indelman, “Bayesian Viewpoint-Dependent Robust Classification under Model and Localization Uncertainty,” in IEEE International Conference on Robotics and Automation (ICRA), May 2018.
    Feldman18icra.pdf Feldman18icra.poster
  25. Y. Feldman and V. Indelman, “Towards Robust Autonomous Semantic Perception,” in Workshop on Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding, in conjunction with IEEE International Conference on Robotics and Automation (ICRA), May 2018.
    Feldman18icra_ws.pdf
  26. A. Kitanov and V. Indelman, “Topological Multi-Robot Belief Space Planning in Unknown Environments,” in IEEE International Conference on Robotics and Automation (ICRA), May 2018.
    Kitanov18icra.pdf Kitanov18icra.video Kitanov18icra.poster
  27. D. Kopitkov and V. Indelman, “Bayesian Information Recovery from CNN for Probabilistic Inference,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018.
    Kopitkov18iros.pdf Kopitkov18iros.slides
  28. E. Farhi and V. Indelman, “Towards Efficient Inference Update through Planning via JIP - Joint Inference and Belief Space Planning,” in IEEE International Conference on Robotics and Automation (ICRA), May 2017.
    Farhi17icra.pdf Farhi17icra.slides
  29. K. Elimelech and V. Indelman, “Consistent Sparsification for Efficient Decision Making Under Uncertainty in High Dimensional State Spaces,” in IEEE International Conference on Robotics and Automation (ICRA), May 2017.
    Elimelech17icra.pdf Elimelech17icra.slides
  30. S. Pathak, A. Thomas, and V. Indelman, “Nonmyopic Data Association Aware Belief Space Planning for Robust Active Perception,” in IEEE International Conference on Robotics and Automation (ICRA), May 2017.
    Pathak17icra.pdf Pathak17icra.slides
  31. K. Elimelech and V. Indelman, “Scalable Sparsification for Efficient Decision Making Under Uncertainty in High Dimensional State Spaces,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2017.
    Elimelech17iros.pdf Elimelech17iros.slides
  32. Y. Ben-Elisha and V. Indelman, “Active Online Visual-Inertial Navigation and Sensor Calibration via Belief Space Planning and Factor Graph Based Incremental Smoothing,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2017.
    BenElisha17iros.pdf BenElisha17iros.slides BenElisha17iros.supplementary
  33. K. Elimelech and V. Indelman, “Fast Action Elimination for Efficient Decision Making and Belief Space Planning Using Bounded Approximations,” in International Symposium on Robotics Research (ISRR), Dec. 2017.
    Elimelech17isrr.pdf Elimelech17isrr.slides
  34. V. Indelman, “No Correlations Involved: Decision Making Under Uncertainty in a Conservative Sparse Information Space,” in IEEE International Conference on Robotics and Automation (ICRA), submission via IEEE Robotics and Automation Letters (RA-L), May 2016.
    Indelman16icra.pdf Indelman16icra.slides
  35. S. Pathak, A. Thomas, A. Feniger, and V. Indelman, “Towards Data Association Aware Belief Space Planning for Robust Active Perception,” in AI for Long-term Autonomy, workshop in conjunction with IEEE International Conference on Robotics and Automation (ICRA), May 2016.
    Pathak16icra_ws.pdf Pathak16icra_ws.poster
  36. S. Pathak, A. Thomas, A. Feniger, and V. Indelman, “DA-BSP: Towards Data Association Aware Belief Space Planning for Robust Active Perception,” in European Conference on Artificial Intelligence (ECAI), accepted for short paper presentation, Sep. 2016.
    Pathak16ecai.pdf Pathak16ecai.poster
  37. S. Pathak, S. Soudjani, V. Indelman, and A. Abate, “Formal and Data-association aware Belief Space Planning,” in Eighth European Starting AI Researcher Symposium (STAIRS), co-located with European Conference on Artificial Intelligence (ECAI), Sep. 2016.
    Pathak16stairs.pdf Pathak16stairs.slides
  38. D. Kopitkov and V. Indelman, “Computationally Efficient Active Inference in High-Dimensional State Spaces,” in AI for Long-term Autonomy, workshop in conjunction with IEEE International Conference on Robotics and Automation (ICRA), May 2016.
    Kopitkov16icra_ws.pdf Kopitkov16icra_ws.poster
  39. D. Kopitkov and V. Indelman, “Computationally Efficient Decision Making Under Uncertainty in High-Dimensional State Spaces,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2016.
    Kopitkov16iros.pdf Kopitkov16iros.slides
  40. T. Regev and V. Indelman, “Multi-Robot Decentralized Belief Space Planning in Unknown Environments via Efficient Re-Evaluation of Impacted Paths,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2016.
    Regev16iros.pdf Regev16iros.slides
  41. J. Dong, E. Nelson, V. Indelman, N. Michael, and F. Dellaert, “Distributed Real-time Cooperative Localization and Mapping using an Uncertainty-Aware Expectation Maximization Approach,” in IEEE International Conference on Robotics and Automation (ICRA), May 2015.
    Dong15icra.pdf Dong15icra.slides Dong15icra.video
  42. S. Choudhary, V. Indelman, H. I. Christensen, and F. Dellaert, “Information-based Reduced Landmark SLAM,” in IEEE International Conference on Robotics and Automation (ICRA), May 2015.
    Choudhary15icra.pdf Choudhary15icra.slides Choudhary15icra.supplementary
  43. V. Indelman, “Towards Information-Theoretic Decision Making in a Conservative Information Space,” in American Control Conference (ACC), Jul. 2015.
    Indelman15acc.pdf Indelman15acc.slides
  44. V. Indelman, “Towards Multi-Robot Active Collaborative State Estimation via Belief Space Planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2015.
    Indelman15iros.pdf Indelman15iros.slides Indelman15iros.video
  45. V. Indelman, “Towards Cooperative Multi-Robot Belief Space Planning in Unknown Environments,” in International Symposium on Robotics Research (ISRR), Sep. 2015.
    Indelman15isrr.pdf Indelman15isrr.slides
  46. X. Yan, V. Indelman, and B. Boots, “Incremental Sparse GP Regression for Continuous-time Trajectory Estimation and Mapping,” in International Symposium on Robotics Research (ISRR), Sep. 2015.
    Yan15isrr.pdf Yan15isrr.slides
  47. V. Indelman, “On Decision Making and Planning in the Conservative Information Space - Is the Concept Applicable to Active SLAM?,” in The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM, workshop in conjunction with Robotics Science and Systems (RSS) Conference, Jul. 2015.
    Indelman15rss_ws_a.pdf Indelman15rss_ws_a.poster
  48. V. Indelman, “On Multi-Robot Active Collaborative Inference in Unknown Environments via Belief Space Planning,” in Principles of Multi-Robot Systems, workshop in conjunction with Robotics Science and Systems (RSS) Conference, Jul. 2015.
    Indelman15rss_ws_b.pdf Indelman15rss_ws_b.poster
  49. X. Yan, V. Indelman, and B. Boots, “Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping,” in The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM, workshop in conjunction with Robotics Science and Systems (RSS) Conference. *Best workshop poster award*, Jul. 2015.
    Yan15rss_ws.pdf Yan15rss_ws.poster
  50. V. Indelman, “Distributed Perception and Estimation: a Short Survey,” in Principles of Multi-Robot Systems, workshop in conjunction with Robotics Science and Systems (RSS) Conference, Jul. 2015.
    Indelman15rss_ws_c.pdf Indelman15rss_ws_c.slides
  51. V. Indelman, E. Nelson, N. Michael, and F. Dellaert, “Distributed Navigation with Unknown Initial Poses and Data Association via Expectation Maximization,” in 56th Israel Annual Conference on Aerospace Sciences, Mar. 2015.
    Indelman15iacas_c.pdf Indelman15iacas_c.slides
  52. L. Carlone, Z. Kira, C. Beall, V. Indelman, and F. Dellaert, “Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors,” in IEEE International Conference on Robotics and Automation (ICRA), Jun. 2014.
    Carlone14icra.pdf
  53. V. Indelman, L. Carlone, and F. Dellaert, “Planning Under Uncertainty in the Continuous Domain: a Generalized Belief Space Approach,” in IEEE International Conference on Robotics and Automation (ICRA), Jun. 2014.
    Indelman14icra_a.pdf Indelman14icra_a.slides
  54. V. Indelman, E. Nelson, N. Michael, and F. Dellaert, “Multi-Robot Pose Graph Localization and Data Association from Unknown Initial Relative Poses via Expectation Maximization,” in IEEE International Conference on Robotics and Automation (ICRA), Jun. 2014.
    Indelman14icra_b.pdf Indelman14icra_b.slides
  55. E. Nelson, V. Indelman, N. Michael, and F. Dellaert, “An Experimental Study of Robust Distributed Multi-Robot Data Association from Arbitrary Poses,” in International Symposium on Experimental Robotics (ISER), Jun. 2014.
    Nelson14iser.pdf Nelson14iser.slides
  56. V. Indelman, N. Michael, and F. Dellaert, “Incremental Distributed Robust Inference from Arbitrary Robot Poses via EM and Model Selection,” in RSS Workshop on Distributed Control and Estimation for Robotic Vehicle Networks, Jul. 2014.
    Indelman14rss_ws.pdf Indelman14rss_ws.poster
  57. X. Yan, V. Indelman, and B. Boots, “Incremental Sparse GP Regression for Continuous-time Trajectory Estimation and Mapping,” in NIPS Workshop on Autonomously Learning Robots, Dec. 2014.
    Yan14nips_ws.pdf Yan14nips_ws.poster
  58. A. Cunningham, V. Indelman, and F. Dellaert, “DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping,” in IEEE International Conference on Robotics and Automation (ICRA), May 2013.
    Cunningham13icra.pdf Cunningham13icra.slides
  59. A. Cunningham, K. Ok, J. Antico, V. Indelman, and F. Dellaert, “Aerial Robot Experimental Design for Decentralized Visual SLAM,” in Unmanned Systems Technology XV - SPIE Defense, Security and Sensing, Apr. 2013.
  60. V. Indelman, A. Melim, and F. Dellaert, “Incremental Light Bundle Adjustment for Robotics Navigation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2013.
    Indelman13iros.pdf Indelman13iros.slides
  61. V. Indelman, L. Carlone, and F. Dellaert, “Towards Planning in Generalized Belief Space,” in International Symposium on Robotics Research (ISRR), Dec. 2013.
    Indelman13isrr.pdf Indelman13isrr.slides
  62. V. Indelman, R. Roberts, and F. Dellaert, “Probabilistic Analysis of Incremental Light Bundle Adjustment,” in IEEE Workshop on Robot Vision (WoRV), *best poster award*, Jan. 2013.
    Indelman13worv.pdf Indelman13worv.slides
  63. V. Indelman, R. Roberts, C. Beall, and F. Dellaert, “Incremental Light Bundle Adjustment,” in British Machine Vision Conference (BMVC), Sep. 2012.
    Indelman12bmvc.pdf Indelman12bmvc.slides Indelman12bmvc.video
  64. V. Indelman, S. Wiliams, M. Kaess, and F. Dellaert, “Factor Graph Based Incremental Smoothing in Inertial Navigation Systems,” in International Conference on Information Fusion, Jul. 2012.
    Indelman12fusion.pdf Indelman12fusion.slides
  65. A. Cunningham, V. Indelman, and F. Dellaert, “Consistent Decentralized Graphical SLAM with Anti-Factor Down-Dating,” in 10th IEEE International Symposium on Safety Security and Rescue Robotics (SSRR), Nov. 2012.
    Cunningham12ssrr.pdf
  66. V. Indelman, “Bundle Adjustment Without Iterative Structure Estimation and its Application to Navigation,” in IEEE/ION Position Location and Navigation System (PLANS) Conference, Apr. 2012.
    Indelman12plans_a.pdf Indelman12plans_a.slides
  67. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Graph-Based Cooperative Navigation Using Three-View Constraints: Method Validation,” in IEEE/ION Position Location and Navigation System (PLANS) Conference, Apr. 2012.
    Indelman12plans_b.pdf
  68. M. Kaess, S. Williams, V. Indelman, R. Roberts, J. J. Leonard, and F. Dellaert, “Concurrent Filtering and Smoothing,” in International Conference on Information Fusion, Jul. 2012.
    Kaess12fusion.pdf Kaess12fusion.slides
  69. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Distributed Vision-Aided Cooperative Localization and Navigation based on Three-View Geometry,” in IEEE Aerospace Conference, Mar. 2011.
    Indelman11aerospace.pdf Indelman11aerospace.slides
  70. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Graph-based Distributed Cooperative Navigation,” in IEEE International Conference on Robotics and Automation (ICRA), May 2011.
    Indelman11icra.pdf Indelman11icra.slides
  71. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Mosaic Aided Navigation: Tools, Methods and Results,” in IEEE/ION Position Location and Navigation System (PLANS) Conference, May 2010.
    Indelman10plans.pdf
  72. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Real-Time Mosaic-Aided Aerial Navigation: I. Motion Estimation,” in AIAA Guidance, Navigation and Control Conference, Aug. 2009.
    Indelman09gnc_a.pdf Indelman09gnc_a.slides
  73. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Real-Time Mosaic-Aided Aerial Navigation: II. Sensor Fusion,” in AIAA Guidance, Navigation and Control Conference, Aug. 2009.
    Indelman09gnc_b.pdf Indelman09gnc_b.slides
  74. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Navigation Aiding Using On-Line Mosaicking,” in IEEE/ION Position Location and Navigation System (PLANS) Conference, May 2008.
    URL: https://ieeexplore.ieee.org/document/4570070
  75. V. Indelman, P. Gurfil, E. Rivlin, and H. Rotstein, “Navigation Performance Enhancement Using Rotation and Translation Measurements from Online Mosaicking,” in AIAA Guidance, Navigation and Control Conference, Aug. 2007.
    DOI: doi.org/10.2514/6.2007-6748

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ArXiv and Technical Reports

  1. R. Mor and V. Indelman, “Probabilistic Qualitative Localization and Mapping,” Feb. 2023.
    arXiv: https://arxiv.org/abs/2302.08735
  2. T. Yotam and V. Indelman, “Measurement Simplification in ρ-POMDP with Performance Guarantees,” Sep. 2023.
    arXiv: https://arxiv.org/abs/2309.10701
  3. A. Zhitnikov, O. Sztyglic, and V. Indelman, “No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification,” 2023.
    arXiv: https://arxiv.org/abs/2310.10274
  4. A. Zhitnikov and V. Indelman, “Risk Aware Belief-dependent Constrained Simplified POMDP Planning,” Sep. 2022.
    arXiv: https://arxiv.org/pdf/2209.02679.pdf
  5. G. Rotman and V. Indelman, “involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs,” Sep. 2022.
    arXiv: https://arxiv.org/pdf/2209.11591.pdf
  6. E. Farhi and V. Indelman, “iX-BSP: Incremental Belief Space Planning,” 2021.
    arXiv: https://arxiv.org/pdf/2102.09539
  7. A. Zhitnikov and V. Indelman, “Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty,” 2021.
    arXiv: https://arxiv.org/pdf/2105.05789.pdf
  8. K. Elimelech and V. Indelman, “Efficient Belief Space Planning in High-Dimensional State Spaces using PIVOT: Predictive Incremental Variable Ordering Tactic,” 2021.
    arXiv: https://arxiv.org/pdf/2112.14428.pdf
  9. O. Sztyglic, A. Zhitnikov, and V. Indelman, “Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency,” May 2021.
    arXiv: https://arxiv.org/pdf/2105.14239.pdf
  10. D. Kopitkov and V. Indelman, “General Probabilistic Surface Optimization and Log Density Estimation,” 2019.
    arXiv: https://arxiv.org/pdf/1903.10567
  11. A. Kitanov and V. Indelman, “Topological Information-Theoretic Belief Space Planning with Optimality Guarantees,” 2019.
    arXiv: https://arxiv.org/pdf/1903.00927
  12. D. Kopitkov and V. Indelman, “General Purpose Incremental Covariance Update and Efficient Belief Space Planning via Factor-Graph Propagation Action Tree,” 2019.
    arXiv: https://arxiv.org/abs/1906.02249
  13. K. Elimelech and V. Indelman, “Efficient Decision Making and Belief Space Planning using Sparse Approximations,” 2019.
    arXiv: https://arxiv.org/abs/1909.00885
  14. D. Kopitkov and V. Indelman, “Deep PDF: Probabilistic Surface Optimization and Density Estimation,” 2018.
    arXiv: http://arxiv.org/abs/1807.10728
  15. S. Pathak, A. Thomas, A. Feniger, and V. Indelman, “Robust Active Perception via Data-association aware Belief Space Planning,” 2016.
    arXiv: http://arxiv.org/abs/1606.05124

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PhD Theses

  1. Y. Feldman, “Semantic Perception under Uncertainty with Viewpoint-Dependent Models,” PhD thesis, Technion - Israel Institute of Technology, 2022.
    Feldman22thesis.pdf Feldman22thesis.slides Feldman22thesis.video
  2. E. Farhi, “Joint Incremental Inference and Belief Space Planning for Online Operations of Autonomous systems,” PhD thesis, Technion - Israel Institute of Technology, 2021.
    Farhi21thesis.pdf Farhi21thesis.slides Farhi21thesis.video
  3. V. Tchuiev, “Autonomous Classification Under Uncertainty,” PhD thesis, Technion - Israel Institute of Technology, 2021.
    Tchuiev21thesis.pdf Tchuiev21thesis.slides Tchuiev21thesis.video
  4. K. Elimelech, “Efficient Decision Making under Uncertainty in High-Dimensional State Spaces,” PhD thesis, Technion - Israel Institute of Technology, 2021.
    Elimelech21thesis.pdf Elimelech21thesis.slides Elimelech21thesis.video
  5. D. Kopitkov, “General Probabilistic Surface Optimization and Log Density Estimation,” PhD thesis, Technion - Israel Institute of Technology, 2020.
    Kopitkov20thesis.pdf Kopitkov20thesis.slides Kopitkov20thesis.video
  6. V. Indelman, “Navigation Performance Enhancement Using Online Mosaicking,” PhD thesis, Technion - Israel Institute of Technology, 2011.
    Indelman11thesis.pdf Indelman11thesis.slides

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Master’s Theses

  1. T. Yotam, “Measurement Simplification in ρ-POMDP with Performance Guarantees,” Master's thesis, Technion - Israel Institute of Technology, 2023.
    Yotam23thesis.pdf Yotam23thesis.slides Yotam23thesis.video
  2. O. Shelly, “Hypotheses disambiguation in retrospective for robust perception in ambiguous environments,” Master's thesis, Technion - Israel Institute of Technology, 2022.
    Shelly22thesis.pdf Shelly22thesis.slides Shelly22thesis.video
  3. I. Zilberman, “Belief Space Planning using Qualitative Spatial Relationships,” Master's thesis, Technion - Israel Institute of Technology, 2022.
    Zilberman22thesis.pdf Zilberman22thesis.slides Zilberman22thesis.video
  4. G. Rotman, “Efficient Informative Planning with High-dimensional Non-Gaussian Beliefs by Exploiting Structure,” Master's thesis, Technion - Israel Institute of Technology, 2022.
    Rotman22thesis.pdf Rotman22thesis.slides Rotman22thesis.video
  5. R. Mor, “Probabilistic Qualitative Localization and Mapping,” Master's thesis, Technion - Israel Institute of Technology, 2022.
    Mor22thesis.pdf Mor22thesis.slides Mor22thesis.video
  6. O. Sztyglic, “Online Partially Observable Markov Decision Process Planning via Simplification,” Master's thesis, Technion - Israel Institute of Technology, 2021.
    Sztyglic21thesis.pdf Sztyglic21thesis.slides Sztyglic21thesis.video
  7. O. Asraf, “Experience-Based Prediction of Unknown Environments for Enhanced Belief Space Planning,” Master's thesis, Technion - Israel Institute of Technology, 2020.
    Asraf20thesis.pdf Asraf20thesis.slides
  8. V. Ovechkin, “Bundle Adjustment with Feature Scale Constraints for Enhanced Estimation Accuracy,” Master's thesis, Technion - Israel Institute of Technology, 2018.
    Ovechkin18thesis.pdf Ovechkin18thesis.slides
  9. S. Har-Nes, “Belief Space Planning for Autonomous Navigation while Modeling Landmark Identification,” Master's thesis, Technion - Israel Institute of Technology, 2017.
    HarNes17thesis.pdf HarNes17thesis.slides
  10. A. Thomas, “Incorporating Data Association Within Belief Space Planning For Robust Autonomous Navigation,” Master's thesis, Technion - Israel Institute of Technology, 2017.
    Thomas17thesis.pdf Thomas17thesis.slides
  11. M. Chojnacki, “Vision-based Target Tracking and Ego-Motion Estimation using Incremental Light Bundle Adjustment,” Master's thesis, Technion - Israel Institute of Technology, 2017.
    Chojnacki17thesis.pdf Chojnacki17thesis.slides
  12. D. Kopitkov, “Efficient Belief Space Planning in High-dimensional State Spaces by Exploiting Sparsity and Calculation Re-use,” Master's thesis, Technion - Israel Institute of Technology, 2017.
    Kopitkov17thesis.pdf Kopitkov17thesis.slides
  13. Y. Ben-Elisha, “Cooperative Multi-Robot Belief Space Planning for Visual-Inertial Navigation and Online Sensor Calibration,” Master's thesis, Technion - Israel Institute of Technology, 2017.
    BenElisha17thesis.pdf BenElisha17thesis.slides
  14. T. Regev, “Multi-Robot Decentralized Belief Space Planning in Unknown Environments,” Master's thesis, Technion - Israel Institute of Technology, 2016.
    Regev16thesis.pdf Regev16thesis.slides

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