  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.
