Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization (2401.15313v3)

Published 27 Jan 2024 in cs.RO, cs.CV, cs.SY, eess.SY, and math.OC

Abstract: In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (126)
  1. P. Yang, R. A. Freeman, and K. M. Lynch, “Multi-agent coordination by decentralized estimation and control,” IEEE Transactions on Automatic Control, vol. 53, no. 11, pp. 2480–2496, 2008.
  2. K. Sakurama, T. Sugie et al., “Generalized coordination of multi-robot systems,” Foundations and Trends® in Systems and Control, vol. 9, no. 1, pp. 1–170, 2021.
  3. S. Roumeliotis and G. Bekey, “Distributed multirobot localization,” IEEE Transactions on Robotics and Automation, vol. 18, no. 5, pp. 781–795, 2002.
  4. A. Howard, “Multi-robot simultaneous localization and mapping using particle filters,” The International Journal of Robotics Research, vol. 25, no. 12, pp. 1243–1256, 2006.
  5. S. Thrun and Y. Liu, “Multi-robot slam with sparse extended information filers,” in Robotics Research. The Eleventh International Symposium, 2005, pp. 254–266.
  6. J. P. Queralta, J. Taipalmaa, B. Can Pullinen, V. K. Sarker, T. Nguyen Gia, H. Tenhunen, M. Gabbouj, J. Raitoharju, and T. Westerlund, “Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision,” IEEE Access, vol. 8, pp. 191 617–191 643, 2020.
  7. A. Martinelli, F. Pont, and R. Siegwart, “Multi-robot localization using relative observations,” in IEEE International Conference on Robotics and Automation (ICRA), 2005, pp. 2797–2802.
  8. A. Miller, K. Rim, P. Chopra, P. Kelkar, and M. Likhachev, “Cooperative perception and localization for cooperative driving,” in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 1256–1262.
  9. S. Wang, Y. Wang, D. Li, and Q. Zhao, “Distributed relative localization algorithms for multi-robot networks: A survey,” Sensors, vol. 23, no. 5, p. 2399, 2023.
  10. J. Desai, J. Ostrowski, and V. Kumar, “Modeling and control of formations of nonholonomic mobile robots,” IEEE Transactions on Robotics and Automation, vol. 17, no. 6, pp. 905–908, 2001.
  11. A. K. Das, R. Fierro, V. Kumar, J. P. Ostrowski, J. Spletzer, and C. J. Taylor, “A vision-based formation control framework,” IEEE Transactions on Robotics and Automation, vol. 18, no. 5, pp. 813–825, 2002.
  12. M. Garcia-Salguero, J. Briales, and J. Gonzalez-Jimenez, “Certifiable relative pose estimation,” Image and Vision Computing, vol. 109, p. 104142, 2021.
  13. J.-H. Kim and K.-K. K. Kim, “Vision-based markerless robot-to-robot relative pose estimation using RGB-D data,” Journal of Institute of Control, Robotics and Systems, vol. 29, no. 6, pp. 495–0503, 2023.
  14. S. Fang, H. Li, and M. Yang, “Adaptive cubature split covariance intersection filter for multi-vehicle cooperative localization,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1158–1165, 2022.
  15. G. Haynes and H. Hermes, “Nonlinear controllability via Lie theory,” SIAM Journal on Control, vol. 8, no. 4, pp. 450–460, 1970.
  16. R. Hermann and A. Krener, “Nonlinear controllability and observability,” IEEE Transactions on automatic control, vol. 22, no. 5, pp. 728–740, 1977.
  17. A. van der Schaft, “Controllability and observability for affine nonlinear Hamiltonian systems,” IEEE Transactions on Automatic Control, vol. 27, no. 2, pp. 490–492, 1982.
  18. A. Bicchi, D. Prattichizzo, A. Marigo, and A. Balestrino, “On the observability of mobile vehicle localization,” in Theory and Practice of Control and Systems.   World Scientific, 1998, pp. 142–147.
  19. F. Conticelli, A. Bicchi, and A. Balestrino, “Observability and nonlinear observers for mobile robot localization,” IFAC Proceedings Volumes, vol. 33, no. 27, pp. 663–668, 2000.
  20. F. Lorussi, A. Marigo, and A. Bicchi, “Optimal exploratory paths for a mobile rover,” in IEEE International Conference on Robotics and Automation (ICRA), vol. 2, 2001, pp. 2078–2083.
  21. A. Martinelli and R. Siegwart, “Observability analysis for mobile robot localization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2005, pp. 1471–1476.
  22. G. P. Huang, A. I. Mourikis, and S. I. Roumeliotis, “Observability-based rules for designing consistent EKF SLAM estimators,” The International Journal of Robotics Research, vol. 29, no. 5, pp. 502–528, 2010.
  23. G. P. Huang, N. Trawny, A. I. Mourikis, and S. I. Roumeliotis, “Observability-based consistent EKF estimators for multi-robot cooperative localization,” Autonomous Robots, vol. 30, no. 1, pp. 99–122, 2011.
  24. A. Martinelli, “State estimation based on the concept of continuous symmetry and observability analysis: The case of calibration,” IEEE Transactions on Robotics, vol. 27, no. 2, pp. 239–255, 2011.
  25. B. Araki, I. Gilitschenski, T. Ogata, A. Wallar, W. Schwarting, Z. Choudhury, S. Karaman, and D. Rus, “Range-based cooperative localization with nonlinear observability analysis,” in IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 1864–1870.
  26. R. Sharma, R. W. Beard, C. N. Taylor, and S. Quebe, “Graph-based observability analysis of bearing-only cooperative localization,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 522–529, 2012.
  27. J. A. Hesch, D. G. Kottas, S. L. Bowman, and S. I. Roumeliotis, “Camera-IMU-based localization: Observability analysis and consistency improvement,” The International Journal of Robotics Research, vol. 33, no. 1, pp. 182–201, 2014.
  28. G. Panahandeh, C. X. Guo, M. Jansson, and S. I. Roumeliotis, “Observability analysis of a vision-aided inertial navigation system using planar features on the ground,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, pp. 4187–4194.
  29. Y. Yang and G. Huang, “Aided inertial navigation: Unified feature representations and observability analysis,” in International Conference on Robotics and Automation (ICRA), 2019, pp. 3528–3534.
  30. G. Panahandeh, S. Hutchinson, P. Händel, and M. Jansson, “Planar-based visual inertial navigation: Observability analysis and motion estimation,” Journal of Intelligent & Robotic Systems, vol. 82, pp. 277–299, 2016.
  31. M. K. Paul and S. I. Roumeliotis, “Alternating-stereo VINS: Observability analysis and performance evaluation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4729–4737.
  32. Y. Yang and G. Huang, “Observability analysis of aided ins with heterogeneous features of points, lines, and planes,” IEEE Transactions on Robotics, vol. 35, no. 6, pp. 1399–1418, 2019.
  33. J. Huai, Y. Lin, Y. Zhuang, C. K. Toth, and D. Chen, “Observability analysis and keyframe-based filtering for visual inertial odometry with full self-calibration,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 3219–3237, 2022.
  34. J. A. Hesch, D. G. Kottas, S. L. Bowman, and S. I. Roumeliotis, “Observability-constrained vision-aided inertial navigation,” University of Minnesota, Dept. of Comp. Sci. & Eng., MARS Lab, Tech. Rep, vol. 1, p. 6, 2012.
  35. M. A. Gomaa, O. De Silva, G. K. Mann, and R. G. Gosine, “Observability-constrained vins for mavs using interacting multiple model algorithm,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 3, pp. 1423–1442, 2020.
  36. C. Liu, C. Jiang, and H. Wang, “Variable observability constrained visual-inertial-GNSS EKF-based navigation,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6677–6684, 2022.
  37. A. Martinelli, “Nonlinear unknown input observability: Extension of the observability rank condition,” IEEE Transactions on Automatic Control, vol. 64, no. 1, pp. 222–237, 2018.
  38. ——, “Nonlinear unknown input observability and unknown input reconstruction: The general analytical solution,” Information Fusion, vol. 85, pp. 23–51, 2022.
  39. T. Sasaoka, I. Kimoto, Y. Kishimoto, K. Takaba, and H. Nakashima, “Multi-robot SLAM via information fusion extended Kalman filters,” IFAC-PapersOnLine, vol. 49, no. 22, pp. 303–308, 2016.
  40. S. Shreedharan, “DKF-SLAM: Distributed Kalman filtering for multi-robot SLAM,” Ph.D. dissertation, UC San Diego, 2023.
  41. A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint Kalman filter for vision-aided inertial navigation,” in IEEE International Conference on Robotics and Automation (ICRA), 2007, pp. 3565–3572.
  42. I. V. Melnyk, J. A. Hesch, and S. I. Roumeliotis, “Cooperative vision-aided inertial navigation using overlapping views,” in IEEE International Conference on Robotics and Automation (ICRA), 2012, pp. 936–943.
  43. P. Zhu, Y. Yang, W. Ren, and G. Huang, “Cooperative visual-inertial odometry,” in IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 13 135–13 141.
  44. B. Chenchana, O. Labbani-Igbida, S. Renault, and S. Boria, “Range-based collaborative MSCKF localization,” in 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2018, pp. 1–6.
  45. S. Jia, Y. Jiao, Z. Zhang, R. Xiong, and Y. Wang, “FEJ-VIRO: A consistent first-estimate Jacobian visual-inertial-ranging odometry,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 1336–1343.
  46. R. Olfati-Saber, “Distributed Kalman filter with embedded consensus filters,” in Proceedings of the 44th IEEE Conference on Decision and Control (CDC), 2005, pp. 8179–8184.
  47. R. Olfati-Saber and J. S. Shamma, “Consensus filters for sensor networks and distributed sensor fusion,” in Proceedings of the 44th IEEE Conference on Decision and Control (CDC), 2005, pp. 6698–6703.
  48. U. A. Khan and J. M. Moura, “Distributing the Kalman filter for large-scale systems,” IEEE Transactions on Signal Processing, vol. 56, no. 10, pp. 4919–4935, 2008.
  49. R. Olfati-Saber, “Kalman-consensus filter: Optimality, stability, and performance,” in Proceedings of the 48h IEEE Conference on Decision and Control (CDC), 2009, pp. 7036–7042.
  50. F. S. Cattivelli and A. H. Sayed, “Diffusion strategies for distributed kalman filtering and smoothing,” IEEE Transactions on Automatic Control, vol. 55, no. 9, pp. 2069–2084, 2010.
  51. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” IEEE Transactions on Information Theory, vol. 52, no. 6, pp. 2508–2530, 2006.
  52. M. Bosse, G. Agamennoni, and I. Gilitschenski, “Robust estimation and applications in robotics,” Foundations and Trends® in Robotics, vol. 4, no. 4, pp. 225–269, 2016.
  53. J. T. Barron, “A general and adaptive robust loss function,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4331–4339.
  54. P. Bergström and O. Edlund, “Robust registration of point sets using iteratively reweighted least squares,” Computational optimization and applications, vol. 58, no. 3, pp. 543–561, 2014.
  55. G. Grisetti, R. Kümmerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based SLAM,” IEEE Intelligent Transportation Systems Magazine, vol. 2, no. 4, pp. 31–43, 2010.
  56. F. Dellaert, M. Kaess et al., “Factor graphs for robot perception,” Foundations and Trends® in Robotics, vol. 6, no. 1-2, pp. 1–139, 2017.
  57. F. Dellaert, “Factor graphs: Exploiting structure in robotics,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, pp. 141–166, 2021.
  58. Y. Latif, C. Cadena, and J. Neira, “Robust graph slam back-ends: A comparative analysis,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014, pp. 2683–2690.
  59. D. McGann, J. R. III, and M. Kaess, “Robust incremental smoothing and mapping (riSAM),” in IEEE International Conference on Robotics and Automation (ICRA), London, GB, 2023, pp. 4157–4163.
  60. R. Sharma, R. W. Beard, C. N. Taylor, and S. Quebe, “Graph-based observability analysis of bearing-only cooperative localization,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 522–529, 2011.
  61. K. Maes, M. Chatzis, and G. Lombaert, “Observability of nonlinear systems with unmeasured inputs,” Mechanical Systems and Signal Processing, vol. 130, pp. 378–394, 2019.
  62. W.-H. Chen, J. Yang, L. Guo, and S. Li, “Disturbance-observer-based control and related methods—An overview,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 1083–1095, 2015.
  63. K. C. Veluvolu and Y. C. Soh, “High-gain observers with sliding mode for state and unknown input estimations,” IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3386–3393, 2009.
  64. A. Radke and Z. Gao, “A survey of state and disturbance observers for practitioners,” in American Control Conference (ACC), 2006, pp. 5183–5188.
  65. V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler, “Micro-Doppler effect in radar: Phenomenon, model, and simulation study,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 2–21, 2006.
  66. Y. Ma, J. Anderson, S. Crouch, and J. Shan, “Moving object detection and tracking with doppler LiDAR,” Remote Sensing, vol. 11, no. 10, p. 1154, 2019.
  67. W. Sun, Z. Pang, W. Huang, Y. Ji, and Y. Dai, “Vessel velocity estimation and tracking from Doppler echoes of T/RR composite compact HFSWR,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4427–4440, 2021.
  68. D. Kellner, M. Barjenbruch, J. Klappstein, J. Dickmann, and K. Dietmayer, “Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar,” in IEEE Intelligent Vehicles Symposium Proceedings, 2014, pp. 324–329.
  69. D. Kellner, M. Barjenbruch, K. Dietmayer, J. Klappstein, and J. Dickmann, “Instantaneous lateral velocity estimation of a vehicle using Doppler radar,” in Proceedings of the 16th International Conference on Information Fusion, 2013, pp. 877–884.
  70. D. Kellner, M. Barjenbruch, J. Klappstein, J. Dickmann, and K. Dietmayer, “Instantaneous ego-motion estimation using Doppler radar,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013, pp. 869–874.
  71. S. Agarwal, K. Mierle, and The Ceres Solver Team, “Ceres Solver,” 10 2023. [Online]. Available: https://github.com/ceres-solver/ceres-solver
  72. P. J. Rousseeuw and M. Hubert, “Robust statistics for outlier detection,” Wiley interdisciplinary reviews: Data mining and knowledge discovery, vol. 1, no. 1, pp. 73–79, 2011.
  73. M. B. Peterson, P. C. Lusk, and J. P. How, “MOTLEE: Distributed mobile multi-object tracking with localization error elimination,” arXiv preprint arXiv:2304.12175, 2023.
  74. A. Cunningham, M. Paluri, and F. Dellaert, “DDF-SAM: Fully distributed SLAM using constrained factor graphs,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2010, pp. 3025–3030.
  75. A. Cunningham, V. Indelman, and F. Dellaert, “DDF-SAM 2.0: Consistent distributed smoothing and mapping,” in 2013 IEEE international conference on robotics and automation.   IEEE, 2013, pp. 5220–5227.
  76. D. Fox, W. Burgard, H. Kruppa, and S. Thrun, “A probabilistic approach to collaborative multi-robot localization,” Autonomous robots, vol. 8, pp. 325–344, 2000.
  77. S. I. Roumeliotis and I. M. Rekleitis, “Propagation of uncertainty in cooperative multirobot localization: Analysis and experimental results,” Autonomous Robots, vol. 17, no. 1, pp. 41–54, 2004.
  78. L. C. Carrillo-Arce, E. D. Nerurkar, J. L. Gordillo, and S. I. Roumeliotis, “Decentralized multi-robot cooperative localization using covariance intersection,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, pp. 1412–1417.
  79. E. R. Boroson, R. Hewitt, N. Ayanian, and J.-P. de la Croix, “Inter-robot range measurements in pose graph optimization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 4806–4813.
  80. R. Liu, Z. Deng, Z. Cao, M. Shalihan, B. P. L. Lau, K. Chen, K. Bhowmik, C. Yuen, and U.-X. Tan, “Distributed ranging SLAM for multiple robots with Ultra-WideBand and odometry measurements,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 13 684–13 691.
  81. R. C. Smith and P. Cheeseman, “On the representation and estimation of spatial uncertainty,” The international journal of Robotics Research, vol. 5, no. 4, pp. 56–68, 1986.
  82. J. Sola, “Quaternion kinematics for the error-state Kalman filter,” arXiv preprint arXiv:1711.02508, 2017.
  83. A. Barrau and S. Bonnabel, “Invariant Kalman filtering,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, pp. 237–257, 2018.
  84. S. Hauberg, F. Lauze, and K. S. Pedersen, “Unscented kalman filtering on riemannian manifolds,” Journal of mathematical imaging and vision, vol. 46, pp. 103–120, 2013.
  85. M. Brossard, A. Barrau, and S. Bonnabel, “A code for unscented Kalman filtering on manifolds (UKF-M),” in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 5701–5708.
  86. C. Zhang, A. Taghvaei, and P. G. Mehta, “Feedback particle filter on Riemannian manifolds and matrix Lie groups,” IEEE Transactions on Automatic Control, vol. 63, no. 8, pp. 2465–2480, 2017.
  87. K. Li, F. Pfaff, and U. D. Hanebeck, “Unscented dual quaternion particle filter for SE(3) estimation,” IEEE Control Systems Letters, vol. 5, no. 2, pp. 647–652, 2020.
  88. F. Dellaert, “Factor graphs and GTSAM: A hands-on introduction,” Georgia Institute of Technology, Tech. Rep, vol. 2, p. 4, 2012.
  89. Y. Tao and S. S.-T. Yau, “Outlier-robust iterative extended Kalman filtering,” IEEE Signal Processing Letters, vol. 30, pp. 743–747, 2023.
  90. G. Agamennoni, P. Furgale, and R. Siegwart, “Self-tuning M-estimators,” in IEEE International Conference on Robotics and Automation (ICRA), 2015, pp. 4628–4635.
  91. T. Pfeifer and P. Protzel, “Expectation-maximization for adaptive mixture models in graph optimization,” in IEEE International Conference on Robotics and Automation (ICRA), 2019, pp. 3151–3157.
  92. T. Pfeifer, S. Lange, and P. Protzel, “Advancing mixture models for least squares optimization,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3941–3948, 2021.
  93. M. Ramezani, M. Mattamala, and M. Fallon, “AEROS: AdaptivE RObust least-squares for graph-based SLAM,” Frontiers in Robotics and AI, vol. 9, p. 789444, 2022.
  94. C. V. Rao, J. B. Rawlings, and D. Q. Mayne, “Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations,” IEEE transactions on automatic control, vol. 48, no. 2, pp. 246–258, 2003.
  95. J. B. Rawlings and L. Ji, “Optimization-based state estimation: Current status and some new results,” Journal of Process Control, vol. 22, no. 8, pp. 1439–1444, 2012.
  96. J. Civera, O. G. Grasa, A. J. Davison, and J. M. Montiel, “1-point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry,” Journal of field robotics, vol. 27, no. 5, pp. 609–631, 2010.
  97. M. B. Alatise and G. P. Hancke, “Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended Kalman filter,” Sensors, vol. 17, no. 10, 2017.
  98. M. A. Skoglund, G. Hendeby, and D. Axehill, “Extended Kalman filter modifications based on an optimization view point,” in 18th International Conference on Information Fusion (Fusion), 2015, pp. 1856–1861.
  99. L. Carlone, M. K. Ng, J. Du, B. Bona, and M. Indri, “Rao-Blackwellized particle filters multi robot SLAM with unknown initial correspondences and limited communication,” in IEEE International Conference on Robotics and Automation (ICRA), 2010, pp. 243–249.
  100. L. Carlone, M. Kaouk Ng, J. Du, B. Bona, and M. Indri, “Simultaneous localization and mapping using rao-blackwellized particle filters in multi robot systems,” Journal of Intelligent & Robotic Systems, vol. 63, pp. 283–307, 2011.
  101. S. Saeedi, M. Trentini, M. Seto, and H. Li, “Multiple-robot simultaneous localization and mapping: A review,” Journal of Field Robotics, vol. 33, no. 1, pp. 3–46, 2016.
  102. A. Torres-González, J. R. Martinez-de Dios, and A. Ollero, “Range-only SLAM for robot-sensor network cooperation,” Autonomous Robots, vol. 42, no. 3, pp. 649–663, 2018.
  103. N. Funabiki, B. Morrell, J. Nash, and A.-a. Agha-mohammadi, “Range-aided pose-graph-based SLAM: Applications of deployable ranging beacons for unknown environment exploration,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 48–55, 2020.
  104. P.-Y. Lajoie, B. Ramtoula, Y. Chang, L. Carlone, and G. Beltrame, “DOOR-SLAM: Distributed, online, and outlier resilient SLAM for robotic teams,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1656–1663, 2020.
  105. R. T. Rodrigues, N. Tsiogkas, A. Pascoal, and A. P. Aguiar, “Online range-based SLAM using B-spline surfaces,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1958–1965, 2021.
  106. A. Papalia, A. Fishberg, B. W. O’Neill, J. P. How, D. M. Rosen, and J. J. Leonard, “Certifiably correct range-aided SLAM,” arXiv preprint arXiv:2302.11614, 2023.
  107. A. Papalia, J. Morales, K. J. Doherty, D. M. Rosen, and J. J. Leonard, “Score: A second-order conic initialization for range-aided slam,” in IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 10 637–10 644.
  108. F. Dümbgen, C. Holmes, B. Agro, and T. D. Barfoot, “Toward globally optimal state estimation using automatically tightened semidefinite relaxations,” arXiv preprint arXiv:2308.05783, 2023.
  109. C. Holmes, F. Dümbgen, and T. D. Barfoot, “On semidefinite relaxations for matrix-weighted state-estimation problems in robotics,” arXiv preprint arXiv:2308.07275, 2023.
  110. A. Goudar, F. Dümbgen, T. D. Barfoot, and A. P. Schoellig, “Optimal initialization strategies for range-only trajectory estimation,” arXiv preprint arXiv:2309.09011, 2023.
  111. C. Wang, H. Zhang, T.-M. Nguyen, and L. Xie, “Ultra-wideband aided fast localization and mapping system,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 1602–1609.
  112. Y. Song, M. Guan, W. P. Tay, C. L. Law, and C. Wen, “UWB/LiDAR fusion for cooperative range-only SLAM,” in IEEE International Conference on Robotics and Automation (ICRA), 2019, pp. 6568–6574.
  113. T.-M. Nguyen, S. Yuan, M. Cao, T. H. Nguyen, and L. Xie, “VIRAL SLAM: Tightly coupled camera-IMU-UWB-Lidar SLAM,” arXiv preprint arXiv:2105.03296, 2021.
  114. R. Liu, Z. Deng, Z. Cao, M. Shalihan, B. P. L. Lau, K. Chen, K. Bhowmik, C. Yuen, and U.-X. Tan, “Distributed ranging slam for multiple robots with ultra-wideband and odometry measurements,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 13 684–13 691.
  115. T. H. Nguyen, T.-M. Nguyen, and L. Xie, “Range-focused fusion of camera-IMU-UWB for accurate and drift-reduced localization,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1678–1685, 2021.
  116. ——, “Flexible and resource-efficient multi-robot collaborative visual-inertial-range localization,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 928–935, 2021.
  117. C. Zhang, X. Ma, and P. Qin, “LiDAR-IMU-UWB-based collaborative localization,” World Electric Vehicle Journal, vol. 13, no. 2, p. 32, 2022.
  118. T.-M. Nguyen, S. Yuan, M. Cao, Y. Lyu, T. H. Nguyen, and L. Xie, “NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint,” The International Journal of Robotics Research, vol. 41, no. 3, pp. 270–280, 2022.
  119. G. Delama, F. Shamsfakhr, S. Weiss, D. Fontanelli, and A. Fomasier, “UVIO: An UWB-aided visual-inertial odometry framework with bias-compensated anchors initialization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 7111–7118.
  120. J. Wang, P. Gu, L. Wang, and Z. Meng, “RVIO: An effective localization algorithm for range-aided visual-inertial odometry system,” IEEE Transactions on Intelligent Transportation Systems, 2023.
  121. J. Hu, Y. Li, Y. Lei, Z. Xu, M. Lv, and J. Han, “Robust and adaptive calibration of uwb-aided vision navigation system for uavs,” IEEE Robotics and Automation Letters, 2023.
  122. C. Hu, P. Huang, and W. Wang, “Tightly coupled visual-inertial-UWB indoor localization system with multiple position-unknown anchors,” IEEE Robotics and Automation Letters, 2023.
  123. H.-Y. Lin and J.-R. Zhan, “GNSS-denied UAV indoor navigation with UWB incorporated visual inertial odometry,” Measurement, vol. 206, p. 112256, 2023.
  124. A. Goudar, W. Zhao, and A. P. Schoellig, “Range-visual-inertial sensor fusion for micro aerial vehicle localization and navigation,” IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 683–690, 2023.
  125. J. K. Verma and V. Ranga, “Multi-robot coordination analysis, taxonomy, challenges and future scope,” Journal of intelligent & robotic systems, vol. 102, pp. 1–36, 2021.
  126. R. Brooks, “Visual map making for a mobile robot,” in IEEE International Conference on Robotics and Automation (ICRA), vol. 2, 1985, pp. 824–829.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now