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

In urban environments for delivery robots, par- ticularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Address- ing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique fea- tures, 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 compen- sation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localiza- tion performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.