A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
This work addresses the need for a single, robust odometry solution that works across different LiDAR types and environments, eliminating the need for sensor-specific tuning.
The paper proposes a LiDAR-inertial odometry system that works without sensor-specific modeling, using a simplified motion model for IMU integration and direct LiDAR scan-to-map registration with a novel regularization. It achieves robust performance across diverse sensors and platforms with the same configuration, as demonstrated on multiple datasets.
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry system that does not rely on sensor-specific modeling. Sensor fusion techniques for LiDAR and inertial measurement unit (IMU) data typically integrate IMU data iteratively in a Kalman filter or use pre-integration in a factor graph framework, combined with LiDAR scan matching often exploiting some form of feature extraction. We propose an alternative strategy that only requires a simplified motion model for IMU integration and directly registers LiDAR scans in a scan-to-map approach. Our approach allows us to impose a novel regularization on the LiDAR registration, improving the overall odometry performance. We detail extensive experiments on a number of datasets covering a wide array of commonly used robotic sensors and platforms. We show that our approach works with the exact same configuration in all these scenarios, demonstrating its robustness. We have open-sourced our implementation so that the community can build further on our work and use it in their navigation stacks.