Certifiably-Correct Mapping for Safe Navigation Despite Odometry Drift
Devansh R. Agrawal, Rajiv Govindjee, Taekyung Kim, Trushant Adeshara, Jiangbo Yu, Anurekha Ravikumar, Dimitra Panagou
Abstract
Accurate perception, state estimation and mapping are essential for safe robotic navigation as planners and controllers rely on these components for safety critical decisions. However, existing mapping approaches often assume perfect pose estimates, an unrealistic assumption that can lead to incorrect obstacle maps and therefore collisions. This paper introduces a framework for certifiably-correct mapping that ensures that the obstacle map correctly classifies obstacle-free regions despite the odometry drift in vision-based localization systems (VIO/SLAM). By deflating the safe region based on the incremental odometry error at each timestep, we ensure that the map remains accurate and reliable locally around the robot, even as the overall odometry error with respect to the inertial frame grows unbounded.
BibTeX
@inproceedings{rss2025_certifiablycorre,
title = {Certifiably-Correct Mapping for Safe Navigation Despite Odometry Drift},
author = {Devansh R. Agrawal and Rajiv Govindjee and Taekyung Kim and Trushant Adeshara and Jiangbo Yu and Anurekha Ravikumar and Dimitra Panagou},
booktitle = {RSS 2025},
year = {2025}
}