Inter-Robot Range Measurements in Pose Graph Optimization

For multiple robots performing exploration in a previously unmapped environment, such as planetary exploration, maintaining accurate localization and building a consistent map are vital. If the robots do not have a map to localize against and do not explore the same area, they may not be able to find visual loop closures to constrain their relative poses, making traditional SLAM impossible. This paper presents a method for using UWB ranging sensors in multi-robot SLAM, which allows the robots to localize and build a map together even without visual loop closures. The ranging measurements are added to the pose graph as edges and used in optimization to estimate the robots' relative poses. This method builds a map using all robots' observations that is consistent and usable. It performs similarly to visual loop closures when they are available, and provides a good map when they are not, which other methods cannot do. The method is demonstrated on PUFFER robots, developed for autonomous planetary exploration, in an unstructured environment.

Authors

• Elizabeth R. Boroson
• Robert Hewitt
• Nora Ayanian
• Jean-Pierre de la Croix

Keywords

• Visualization
• Simultaneous localization and mapping
• Extraterrestrial measurements
• Distance measurement
• Sensors
• Robots
• Optimization
• Pose Graph
• Pose Graph Optimization
• Range Of Sensors
• Relative Pose
• Loop Closure
• Multiple Robots
• Planetary Exploration
• Linear Function
• True Value
• Tracking System
• Point Cloud
• Kalman Filter
• Base Station
• Global Map
• Local Map
• Inertial Measurement Unit
• Pose Estimation
• Switching Function
• Stereo Camera
• Range Edge
• Visual Odometry
• Visual-inertial Odometry
• Robot Trajectory
• Robot Operating System
• Single Robot
• Scale Error