Enabling autonomous robots to operate robustly in challenging environments is necessary in a future with increased autonomy. For many autonomous systems, estimation and odometry remains a single point of failure, from which it can often be difficult, if not impossible, to recover. As such robust odometry solutions are of key importance. In this work a method for tightly-coupled LiDAR-Radar-Inertial fusion for odometry is proposed, enabling the mitigation of the effects of LiDAR degeneracy by leveraging a complementary perception modality while preserving the accuracy of LiDAR in well-conditioned environments. The proposed approach combines modalities in a factor graph-based windowed smoother with sensor information-specific factor formulations which enable, in the case of degeneracy, partial information to be conveyed to the graph along the non-degenerate axes. The proposed method is evaluated in real-world tests on a flying robot experiencing degraded conditions including geometric self-similarity as well as obscurant occlusion. For the benefit of the community we release the datasets presented: https://github.com/ntnu-arl/lidar_degeneracy_datasets.

翻译：为使自主机器人在未来高自主化场景中稳健运行，必须克服复杂环境的挑战。对许多自主系统而言，状态估计与里程计仍是单点故障源，一旦失效往往难以甚至无法恢复，因此鲁棒里程计解决方案至关重要。本文提出一种紧耦合LiDAR-雷达-惯性融合里程计方法，通过引入互补感知模态缓解LiDAR退化效应，同时保持良好环境中的LiDAR精度。该方法基于因子图的滑动窗口平滑器实现多模态融合，并针对各传感器信息设计特定因子形式，使得在退化情况下能够沿非退化轴将部分信息传递至因子图。我们通过飞行器在几何自相似与遮蔽物遮挡等退化条件下的真实场景实验进行了验证。为促进行业发展，本文公开了实验数据集：https://github.com/ntnu-arl/lidar_degeneracy_datasets。