LocNet: Global localization in 3D point clouds for mobile vehicles

TL;DR

This paper introduces LocNet, a novel semi-handcrafted learning approach for global vehicle localization in 3D LiDAR point clouds, achieving high accuracy in diverse datasets.

Contribution

It presents a new semi-handcrafted representation learning method for LiDAR data and a global localization framework using range-only observations.

Findings

High accuracy on KITTI dataset

Effective in long-term multi-session scenarios

Outperforms existing algorithms in localization precision

Abstract

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.