A Coarse to Fine 3D LiDAR Localization with Deep Local Features for Long Term Robot Navigation in Large Environments

TL;DR

This paper introduces a coarse-to-fine 3D LiDAR localization method combining probabilistic global localization with deep learning-based local features, enabling accurate long-term robot navigation in large, dynamic environments.

Contribution

It presents MCL-DLF, a novel localization approach integrating deep local features from MinkUNeXt into Monte Carlo Localization for improved accuracy in large-scale, seasonal environments.

Findings

MCL-DLF achieves high localization accuracy in large, dynamic environments.

The method outperforms classical ICP and other state-of-the-art techniques.

Deep local features significantly enhance precise point cloud registration.

Abstract

The location of a robot is a key aspect in the field of mobile robotics. This problem is particularly complex when the initial pose of the robot is unknown. In order to find a solution, it is necessary to perform a global localization. In this paper, we propose a method that addresses this problem using a coarse-to-fine solution. The coarse localization relies on a probabilistic approach of the Monte Carlo Localization (MCL) method, with the contribution of a robust deep learning model, the MinkUNeXt neural network, to produce a robust description of point clouds of a 3D LiDAR within the observation model. For fine localization, global point cloud registration has been implemented. MinkUNeXt aids this by exploiting the outputs of its intermediate layers to produce deep local features for each point in a scan. These features facilitate precise alignment between the current sensor observation and one of the point clouds on the map. The proposed MCL method incorporating Deep Local Features for fine localization is termed MCL-DLF. Alternatively, a classical ICP method has been implemented for this precise localization aiming at comparison purposes. This method is termed MCL-ICP. In order to validate the performance of MCL-DLF method, it has been tested on publicly available datasets such as the NCLT dataset, which provides seasonal large-scale environments. Additionally, tests have been also performed with own data (UMH) that also includes seasonal variations on large indoor/outdoor scenarios. The results, which were compared with established state-of-the-art methodologies, demonstrate that the MCL-DLF method obtains an accurate estimate of the robot localization in dynamic environments despite changes in environmental conditions. For reproducibility purposes, the code is publicly available at https://github.com/miriammaximo/MCL-DLF.git