Laser map aided visual inertial localization in changing environment

TL;DR

This paper introduces a novel visual inertial localization framework that leverages LiDAR-built maps and hybrid bundle adjustment to improve long-term outdoor localization across changing environments and seasons.

Contribution

It proposes a hybrid bundle adjustment framework and multi-session map optimization for more accurate cross-modal data association in long-term outdoor localization.

Findings

Achieves satisfactory localization results across different seasons.

Demonstrates superiority of hybrid bundle adjustment over traditional methods.

Validates effectiveness through data collected in campus environments.

Abstract

Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geometry information of the laser map, a hybrid bundle adjustment framework is proposed, which estimates the poses of the cameras with respect to the prior laser map as well as optimizes the state variables of the online visual inertial odometry system simultaneously. For more accurate cross-modal data association, the laser map is optimized using multi-session laser and visual data to extract the salient and stable subset for localization. To validate the efficiency of the proposed method, we collect data in south part of our campus in different seasons, along the same and opposite-direction route. In all sessions of localization data, our proposed method gives satisfactory results, and shows the superiority of the hybrid bundle adjustment and map optimization.