A Slope-Based Targetless Extrinsic Calibration Method for LiDAR-IMU Systems on Ground Vehicles

Extrinsic calibration is a fundamental prerequisite for the effective combined use of light detection and ranging (LiDAR) and inertial measurement units (IMUs). However, most existing targetless LiDAR-IMU calibration methods require full motion to derive the calibration parameters, which ground vehicles (GVs) cannot provide. Existing car calibration methods use additional sensors or prior knowledge to enhance their performance, which increases the workload and introduces additional calibration errors caused by inaccurate prior information. To address this issue, we propose a slope-based targetless LiDAR-IMU extrinsic calibration method that enhances in-vehicle LiDAR-IMU calibration performance without needing additional sensors or prior knowledge. First, we demonstrate that the unobservable direction caused by planar motion can be addressed by leveraging a slope trajectory. Second, we propose a targetless extrinsic calibration method within a continuous-time batch optimization framework for datasets using slope trajectories. Experiments in both simulated and real-world environments have been conducted to prove that slope trajectories enhance calibration performance. The results show the competitive accuracy and consistency of the proposed method, particularly in terms of the vertical extrinsic translation.