IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

In this article, we present inertial lidar localization autocalibration and mapping: an offline probabilistic framework for localization, mapping, and extrinsic calibration based on a 3-D lidar and a six-degree-of-freedom inertial measurement unit. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3-D 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 characterize 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 optimization 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.

Automated summary

This article introduces an offline probabilistic framework for localization, mapping, and extrinsic calibration based on a 3-D lidar and a six-degree-of-freedom inertial measurement unit. The proposed method leverages preintegration and full batch optimization to eliminate motion distortion, achieving automatic calibration and registration of lidar data.