MOTLEE: Collaborative Multi-Object Tracking Using Temporal Consistency for Neighboring Robot Frame Alignment
arxiv(2024)
摘要
Knowing the locations of nearby moving objects is important for a mobile
robot to operate safely in a dynamic environment. Dynamic object tracking
performance can be improved if robots share observations of tracked objects
with nearby team members in real-time. To share observations, a robot must make
up-to-date estimates of the transformation from its coordinate frame to the
frame of each neighbor, which can be challenging because of odometry drift. We
present Multiple Object Tracking with Localization Error Elimination (MOTLEE),
a complete system for a multi-robot team to accurately estimate frame
transformations and collaboratively track dynamic objects. To accomplish this,
robots use open-set image-segmentation methods to build object maps of their
environment and then use our Temporally Consistent Alignment of Frames Filter
(TCAFF) to align maps and estimate coordinate frame transformations without any
initial knowledge of neighboring robot poses. We show that our method for
aligning frames enables a team of four robots to collaboratively track six
pedestrians with accuracy similar to that of a system with ground truth
localization in a challenging hardware demonstration. The code and hardware
dataset are available at https://github.com/mit-acl/motlee.
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