A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments

TL;DR

This paper introduces a comprehensive laser-inertial odometry and mapping approach tailored for large-scale highway environments, emphasizing real-time performance, robustness, and low drift through a multi-module system.

Contribution

It presents a novel multi-module system combining scan pre-processing, dynamic object removal, laser-inertial fusion, and precise mapping, outperforming existing methods in highway scenarios.

Findings

Outperforms LOAM and SuMa in highway environments

Achieves real-time, low-drift pose estimation

Provides competitive accuracy on KITTI dataset

Abstract

In this paper, we propose a novel laser-inertial odometry and mapping method to achieve real-time, low-drift and robust pose estimation in large-scale highway environments. The proposed method is mainly composed of four sequential modules, namely scan pre-processing module, dynamic object detection module, laser-inertial odometry module and laser mapping module. Scan pre-processing module uses inertial measurements to compensate the motion distortion of each laser scan. Then, the dynamic object detection module is used to detect and remove dynamic objects from each laser scan by applying CNN segmentation network. After obtaining the undistorted point cloud without moving objects, the laser inertial odometry module uses an Error State Kalman Filter to fuse the data of laser and IMU and output the coarse pose estimation at high frequency. Finally, the laser mapping module performs a fine processing step and the "Frame-to-Model" scan matching strategy is used to create a static global map. We compare the performance of our method with two state-ofthe-art methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the state-of-the-art methods in real highway environments and achieves competitive accuracy on the KITTI dataset.