Accurate and reliable odometry (the estimation of robot movement) is essential in autonomous robotic behaviors. Currently, LiDAR sensors are used to provide high-fidelity, long-range 3D measurements. However, they can struggle in difficult settings, like in the presence of fog, dust, and smoke, or the lack of prominent perceptual features.
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A recent study proposes LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings). It enables robust real-time odometry in perceptually-stressing settings. Diverse sensor inputs are connected in a loosely-coupled switching scheme so that the system can withstand the loss or fail of some sensor channels. Moreover, it can be flexibly adapted to different systems with varying sensor inputs and computational. Experiments show the superiority of LOCUS in terms of accuracy, computation time, and robustness when compared to state-of-the-art algorithms.
A reliable odometry source is a prerequisite to enable complex autonomy behaviour in next-generation robots operating in extreme environments. In this work, we present a high-precision lidar odometry system to achieve robust and real-time operation under challenging perceptual conditions. LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings), provides an accurate multi-stage scan matching unit equipped with an health-aware sensor integration module for seamless fusion of additional sensing modalities. We evaluate the performance of the proposed system against state-of-the-art techniques in perceptually challenging environments, and demonstrate top-class localization accuracy along with substantial improvements in robustness to sensor failures. We then demonstrate real-time performance of LOCUS on various types of robotic mobility platforms involved in the autonomous exploration of the Satsop power plant in Elma, WA where the proposed system was a key element of the CoSTAR team’s solution that won first place in the Urban Circuit of the DARPA Subterranean Challenge.