Lidar SLAM for Autonomous Driving Vehicles

TL;DR

This paper develops a lidar-based SLAM system for autonomous vehicles that integrates landmark and IMU data using an adaptive Kalman filter, with real-time capable covariance estimation and observability analysis.

Contribution

It introduces a self-tuning filter that estimates calibration and noise parameters, and derives closed-form matrices for real-time implementation, enhancing lidar SLAM robustness.

Findings

The system remains observable under certain landmark alignment conditions.

Closed-form matrices enable real-time SLAM processing.

Self-tuning filter improves calibration and noise estimation.

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.