Reliable offroad autonomy requires low-latency, high-accuracy state estimates of pose as well as velocity, which remain viable throughout environments with sub-optimal operating conditions for the utilized perception modalities. As state estimation remains a single point of failure system in the majority of aspiring autonomous systems, failing to address the environmental degradation the perception sensors could potentially experience given the operating conditions, can be a mission-critical shortcoming. In this work, a method for integration of radar velocity information in a LiDAR-inertial odometry solution is proposed, enabling consistent estimation performance even with degraded LiDAR-inertial odometry. The proposed method utilizes the direct velocity-measuring capabilities of an Frequency Modulated Continuous Wave (FMCW) radar sensor to enhance the LiDAR-inertial smoother solution onboard the vehicle through integration of the forward velocity measurement into the graph-based smoother. This leads to increased robustness in the overall estimation solution, even in the absence of LiDAR data. This method was validated by hardware experiments conducted onboard an all-terrain vehicle traveling at high speed, ~12 m/s, in demanding offroad environments.

翻译：可靠的越野自主导航需要在低延迟和高精度条件下实现位姿与速度的状态估计，且需在感知模态面临次优运行条件的各类环境中保持有效性。由于状态估计仍是大多数自主系统中的单点故障环节，若未能解决感知传感器因运行条件可能遭遇的环境退化问题，可能成为危及任务的关键缺陷。本文提出一种将雷达速度信息集成到激光雷达-惯性里程计解决方案中的方法，即使激光雷达-惯性里程计性能退化，仍能实现一致的状态估计性能。该方法利用调频连续波（FMCW）雷达传感器直接测量速度的能力，通过将前向速度测量值集成到基于图优化的平滑器中，增强车载激光雷达-惯性平滑器的性能。即便在缺失激光雷达数据的情况下，该方法仍能提升整体估计解决方案的鲁棒性。研究通过全地形车辆在严苛越野环境中高速（约12米/秒）行驶的硬件实验验证了该方法的有效性。