Grad-Lidar-SLAM: Fully Differentiable Global SLAM for Lidar with Pose-Graph OptimizationDownload PDF

Published: 12 Oct 2022, Last Modified: 05 May 2023PRDL 2022 PosterReaders: Everyone
Keywords: SLAM, Lidar, Deep Learning
TL;DR: We propose differentiable LiDAR based SLAM and differentiable pose graph optimization framework
Abstract: While Lidar-based SLAM systems are critical for autonomous vehicles, most existing methods are non-differentiable. This limits their use with deep neural networks to learn valuable representations. In this work, we propose Grad-Lidar-SLAM, a novel, fully differentiable SLAM framework for Lidar. We show its effectiveness by using it to solve point cloud completion task. We also show that when adapted for Lidar-based SLAM, existing differentiable baselines fail to perform well on real-world datasets. We address this problem by proposing a novel differentiable pose graph optimization framework. Our experiments on real-world datasets show that our proposed approaches outperform the baselines in static and dynamic environments.
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