Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments

TL;DR

This paper introduces NSM, a laser-based localization algorithm that effectively operates in both urban and natural environments by segmenting stable objects and matching key poses, outperforming existing methods especially in vegetated areas.

Contribution

The paper presents a novel feature extraction and matching approach tailored for natural environments, improving localization reliability in cluttered and structure-poor settings.

Findings

Achieves reliable place recognition in forests and urban areas.

Outperforms current state-of-the-art methods in diverse environments.

Effective in environments with heavy foliage and clutter.

Abstract

In this work we introduce Natural Segmentation and Matching (NSM), an algorithm for reliable localization, using laser, in both urban and natural environments. Current state-of-the-art global approaches do not generalize well to structure-poor vegetated areas such as forests or orchards. In these environments clutter and perceptual aliasing prevents repeatable extraction of distinctive landmarks between different test runs. In natural forests, tree trunks are not distinctive, foliage intertwines and there is a complete lack of planar structure. In this paper we propose a method for place recognition which uses a more involved feature extraction process which is better suited to this type of environment. First, a feature extraction module segments stable and reliable object-sized segments from a point cloud despite the presence of heavy clutter or tree foliage. Second, repeatable oriented key poses are extracted and matched with a reliable shape descriptor using a Random Forest to estimate the current sensor's position within the target map. We present qualitative and quantitative evaluation on three datasets from different environments - the KITTI benchmark, a parkland scene and a foliage-heavy forest. The experiments show how our approach can achieve place recognition in woodlands while also outperforming current state-of-the-art approaches in urban scenarios without specific tuning.