We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform incremental motion in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair.
@article{arxiv.2210.01154,
title = {M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance},
author = {Sandipan Das and Navid Mahabadi and Maurice Fallon and Saikat Chatterjee},
journal= {arXiv preprint arXiv:2210.01154},
year = {2023}
}
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For associated video check https://youtu.be/-xSbfaroEPs