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IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping
IEEE Transactions on Robotics ( IF 7.8 ) Pub Date : 2021-02-01 , DOI: 10.1109/tro.2020.3018641
Cedric Le Gentil , Teresa Vidal-Calleja , Shoudong Huang

In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): an offline probabilistic framework for localisation, mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3D-points in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterise the system's motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system's pose, velocity, biases, and time-shift are estimated via a full batch optimisation that includes automatically generated loop-closure constraints. The autocalibration and the registration of lidar data rely on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

中文翻译:

IN2LAAMA:惯性激光雷达定位自动校准和映射

在本文中,我们介绍了惯性激光雷达定位自动校准和映射 (IN2LAAMA):一种基于 3D 激光雷达和 6-DoF-IMU 的用于定位、映射和外部校准的离线概率框架。当今的大多数激光雷达通过在其视野范围内扫描激光来收集有关周围环境的几何信息。因此,一次激光雷达扫描中的 3D 点是在不同的时间戳获取的。如果传感器轨迹不准确,扫描就会受到称为运动失真的现象的影响。所提出的方法利用预积分与惯性测量的连续表示来表征系统在任何时间点的运动。它可以在不依赖任何明确的运动模型的情况下精确校正运动失真。系统的位姿、速度、偏差和时移是通过包含自动生成的闭环约束的全批量优化来估计的。激光雷达数据的自动校准和配准依赖于扫描对之间匹配的平面和边缘特征。该框架的性能通过模拟和真实数据实验得到验证。
更新日期:2021-02-01
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