在连续时间框架下将地面约束的激光雷达-惯性测量单元（LiDAR-IMU）标定扩展至倾斜表面
Extending Ground-Constraint LiDAR-IMU Calibration to Tilted Surfaces in a Continuous-Time Framework
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中文总结 AI 辅助
该研究提出一种无需假设地面平坦的连续时间框架下的无目标 LiDAR-IMU 标定方法，可扩展至倾斜表面，经多数据集验证重复性提升且已开源。
中文摘要 AI 辅助
本文提出一种 novel 方法，将地面车辆的无目标 LiDAR-IMU 标定扩展至非平坦环境。标定通常需要传感器装置完全激励，而地面车辆正常运行时无法满足这一要求。为解决退化平面运动问题，现有最先进方法提出的残差假设重力与物理表面法向量共线，限制了其仅适用于假设地面平坦的场景。本文提出的地面平面残差无需该假设，可应用于倾斜表面上的平面运动。在 Husky 地面车辆采集的数据集、M2DGR 数据集以及越野车辆数据集上验证了结果，在倾斜和地面平坦场景下均显示重复性得到提升，且倾斜场景下提升显著。代码与实验已开源，网址为 this https URL。
英文摘要
This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments. Calibration typically necessitates full exci- tation of the sensor rig, a requirement that is not fulfilled by ground vehicles in normal operation. To address the degenerate planar motion, state-of-the-art methods propose residuals that assume the colinearity of the gravity and physical surface normal vectors, restricting usage to cases where the ground is assumed flat. This paper proposes ground-plane residuals that do not require this assumption, and are applicable for planar motion on a tilted surface. Results are demonstrated on a dataset collected from a Husky ground vehicle, on the M2DGR dataset, as well as on an offroad vehicle dataset. Repeatability is shown to be improved both in tilted and flat-ground scenarios, with strong improvement demonstrated for the tilted case. The implementation and experiments are open-sourced at https://github.com/vkorotkine/licalib_tilted_ground.
发表机构
- McGill University（麦吉尔大学）
- Rheinmetall Provectus（莱茵金属普罗弗克特斯公司）
机构由 AI 辅助整理，请以论文原文为准。
