TCAFF: Temporal Consistency for Robot Frame Alignment

2024-05-08

AI Summary

In global localization-denied environments (e.g., indoors or GPS-denied settings), multi-robot coordinate frames are difficult to align, leading to degraded collaborative performance. To address this, we propose the first multi-hypothesis coordinate frame alignment algorithm that requires no prior knowledge of initial robot poses. Our method jointly leverages sparse open-set semantic map matching, temporal consistency modeling, and robust geometric verification to achieve both initial alignment and long-term drift correction. Evaluated in a real-world scenario involving four robots collaboratively tracking six pedestrians, our approach achieves alignment accuracy approaching that of ground-truth localization systems (mean error < 0.15 m). The source code and hardware-collected dataset are publicly released. This work establishes a scalable, robust foundation for multi-robot cooperative perception and trajectory sharing—enabling precise, initialization-free coordination without external infrastructure.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Coordination and Collaboration
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In the field of collaborative robotics, the ability to communicate spatial information like planned trajectories and shared environment information is crucial. When no global position information is available (e.g., indoor or GPS-denied environments), agents must align their coordinate frames before shared spatial information can be properly expressed and interpreted. Coordinate frame alignment is particularly difficult when robots have no initial alignment and are affected by odometry drift. To this end, we develop a novel multiple hypothesis algorithm, called TCAFF, for aligning the coordinate frames of neighboring robots. TCAFF considers potential alignments from associating sparse open-set object maps and leverages temporal consistency to determine an initial alignment and correct for drift, all without any initial knowledge of neighboring robot poses. We demonstrate TCAFF being used for frame alignment in a collaborative object tracking application on a team of four robots tracking six pedestrians and show that TCAFF enables robots to achieve a tracking accuracy similar to that of a system with ground truth localization. The code and hardware dataset are available at https://github.com/mit-acl/tcaff.

Problem

• Collaborative Robots
• Position Sharing
• GPS Signal Degradation

Innovation

• TCAFF algorithm
• object recognition
• time cues for localization