Salience-guided Ground Factor for Robust Localization of Delivery Robots in Complex Urban Environments

TL;DR

This paper presents a novel salience-guided ground feature method using Salient Object Detection to improve localization robustness of delivery robots in complex urban environments with non-standard features.

Contribution

It introduces a salience-guided ground feature extraction approach leveraging SOD and MC-IPM for enhanced urban robot localization, addressing challenges of appearance changes and non-standard features.

Findings

Effective extraction of salient ground features in urban scenarios

Improved localization accuracy in complex environments

Robustness against illumination and appearance variations

Abstract

In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure and localization. Traditional geometric feature-based localization is hampered by fluctuating illumination and appearance changes. Our preference for SOD over semantic segmentation sidesteps the intricacies of classifying a myriad of non-standardized urban features. To achieve consistent ground features, the Motion Compensate IPM (MC-IPM) technique is implemented, capitalizing on motion for distortion compensation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localization performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.