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: this https URL.
Abstract (translated)
在城市环境中的配送机器人的配送环境中，特别是在校园和城镇等区域，许多自定义特征超出了标准的道路语义分类。为解决这个挑战，我们的论文介绍了一种利用显著物体检测（SOD）的方法来提取这些独特特征，将它们作为提高机器人循环闭合和局部定位的关键因素。传统的基于几何特征的定位方法受到不断变化的光线和外观变化的影响。我们对SOD的偏好超过了分类非标准化城市特征的复杂性。为了实现一致的地面特征，实施了运动补偿IPM（MC-IPM）技术，利用变形补偿运动来选择最相关的显著地面特征，并通过瞬时计算来选择。为了进行深入评估，我们验证了SOD检测和定位在真实城市场景中的性能。项目页面：https:// this URL。
URL
https://arxiv.org/abs/2405.11855
