MOTLEE: Distributed Mobile Multi-Object Tracking with Localization Error Elimination

We present MOTLEE, a distributed mobile multi-object tracking algorithm that enables a team of robots to collaboratively track moving objects in the presence of localization error. Existing approaches to distributed tracking make limiting assumptions regarding the relative spatial relationship of sensors, including assuming a static sensor network or that perfect localization is available. Instead, we develop an algorithm based on the Kalman-Consensus filter for distributed tracking that properly leverages localization uncertainty in collaborative tracking. Further, our method allows the team to maintain an accurate understanding of dynamic objects in the environment by realigning robot frames and incorporating frame alignment uncertainty into our object tracking formulation. We evaluate our method in hardware on a team of three mobile ground robots tracking four people. Compared to previous works that do not account for localization error, we show that MOTLEE is resilient to localization uncertainties, enabling accurate tracking in distributed, dynamic settings with mobile tracking sensors.

Authors

• Mason B. Peterson
• Parker C. Lusk
• Jonathan P. How

Keywords

• Location awareness
• Uncertainty
• Simultaneous localization and mapping
• Limiting
• Collaboration
• Object detection
• Hardware
• Localization Error
• Sensor Networks
• Tracking Accuracy
• Mobile Robot
• Object Tracking
• Position Uncertainty
• Dynamic Objects
• Mobile Sensors
• Swarm Robotics
• Frame Alignment
• Ground Robots
• Local Method
• Effect Of Error
• Autonomous Vehicles
• Ground Plane
• Local Map
• Data Fusion
• Multiple Sensors
• Maximum A Posteriori
• Multiple Object Tracking
• Local Frame
• Iterative Closest Point
• Track Manager
• Odometry
• Coordinate Frame
• Detection Of Pairs
• Pedestrian Walking
• Intel RealSense
• Translation Error