Lidar SLAM for Autonomous Driving Vehicles
Abstract
This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SLAM system leads to the conclusion that the system remains observable upon a geometrical condition on the alignment of the landmarks.
Cite
@article{arxiv.2208.11855,
title = {Lidar SLAM for Autonomous Driving Vehicles},
author = {Farhad Aghili},
journal= {arXiv preprint arXiv:2208.11855},
year = {2022}
}