  Fully decentralized, multiagent trajectory planners enable complex tasks like
search and rescue or package delivery by ensuring safe navigation in unknown
environments. However, deconflicting trajectories with other agents and
ensuring collision-free paths in a fully decentralized setting is complicated
by dynamic elements and localization uncertainty. To this end, this paper
presents (1) an uncertainty-aware multiagent trajectory planner and (2) an
image segmentation-based frame alignment pipeline. The uncertainty-aware
planner propagates uncertainty associated with the future motion of detected
obstacles, and by incorporating this propagated uncertainty into optimization
constraints, the planner effectively navigates around obstacles. Unlike
conventional methods that emphasize explicit obstacle tracking, our approach
integrates implicit tracking. Sharing trajectories between agents can cause
potential collisions due to frame misalignment. Addressing this, we introduce a
novel frame alignment pipeline that rectifies inter-agent frame misalignment.
This method leverages a zero-shot image segmentation model for detecting
objects in the environment and a data association framework based on geometric
consistency for map alignment. Our approach accurately aligns frames with only
0.18 m and 2.7 deg of mean frame alignment error in our most challenging
simulation scenario. In addition, we conducted hardware experiments and
successfully achieved 0.29 m and 2.59 deg of frame alignment error. Together
with the alignment framework, our planner ensures safe navigation in unknown
environments and collision avoidance in decentralized settings.
