Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements

Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging. We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes. We determine the pose between two robots by minimizing the residual error of the ranging measurements from all UWB nodes. To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization. The optimized pose is then fused with the odometry in a particle filtering for pose tracking among a group of mobile robots. We have conducted extensive experiments to validate the effectiveness of the proposed approach.

[1]  Beakcheol Jang,et al.  Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey , 2019, IEEE Communications Surveys & Tutorials.

[2]  Marko Beko,et al.  RSS-Based Localization in Wireless Sensor Networks Using Convex Relaxation: Noncooperative and Cooperative Schemes , 2015, IEEE Transactions on Vehicular Technology.

[3]  Guido Schroeer,et al.  A Real-Time UWB Multi-Channel Indoor Positioning System for Industrial Scenarios , 2018, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[4]  Yang Hong,et al.  A vision-based indoor positioning method with high accuracy and efficiency based on self-optimized-ordered visual vocabulary , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[5]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[6]  Mitesh Patel,et al.  InFo: Indoor localization using Fusion of Visual Information from Static and Dynamic Cameras , 2019, 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[7]  Lihua Xie,et al.  Ultra-Wideband and Odometry-Based Cooperative Relative Localization With Application to Multi-UAV Formation Control , 2020, IEEE Transactions on Cybernetics.

[8]  Kegen Yu,et al.  A New Quaternion Kalman Filter Based Foot-Mounted IMU and UWB Tightly-Coupled Method for Indoor Pedestrian Navigation , 2020, IEEE Transactions on Vehicular Technology.

[9]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[10]  Nirwan Ansari,et al.  A Survey on Fusion-Based Indoor Positioning , 2020, IEEE Communications Surveys & Tutorials.

[11]  C. Ascher,et al.  Dual IMU Indoor Navigation with particle filter based map-matching on a smartphone , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[12]  Meng Zhang,et al.  Cooperative positioning for emergency responders using self IMU and peer-to-peer radios measurements , 2020, Inf. Fusion.

[13]  Antti Ylä-Jääski,et al.  ViNav: A Vision-Based Indoor Navigation System for Smartphones , 2019, IEEE Transactions on Mobile Computing.

[14]  Chau Yuen,et al.  Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Chunlong He,et al.  Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation , 2020, IEEE Internet of Things Journal.

[16]  Yunfei Chen,et al.  WUB-IP: A High-Precision UWB Positioning Scheme for Indoor Multiuser Applications , 2019, IEEE Systems Journal.

[17]  Boyu Zhou,et al.  Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarm , 2021, ArXiv.

[18]  Reza Malekian,et al.  Improving Positioning Accuracy of the Mobile Laser Scanning in GPS-Denied Environments: An Experimental Case Study , 2019, IEEE Sensors Journal.

[19]  Timothy A. Davis,et al.  On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[21]  Wei Meng,et al.  Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments , 2017 .

[22]  Xiaomin Zhu,et al.  Adapted Error Map Based Mobile Robot UWB Indoor Positioning , 2020, IEEE Transactions on Instrumentation and Measurement.

[23]  Xiukui Li,et al.  A GPS-Based Indoor Positioning System With Delayed Repeaters , 2019, IEEE Transactions on Vehicular Technology.

[24]  Jun Zhou,et al.  A High-Precision and Low-Cost IMU-Based Indoor Pedestrian Positioning Technique , 2020, IEEE Sensors Journal.

[25]  S. Shen,et al.  Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Clark N. Taylor,et al.  Unmanned aerial vehicle relative navigation in GPS denied environments , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).