Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences. We contribute an algorithm which refines an initial set of candidate object detections and produces a refined subset of highly accurate detections using a prior map. We begin by using visual place recognition (VPR) to retrieve a reference map image for a given query image, then use a binary classification neural network that compares the query and mapping image regions to validate the query detection. Once our classification network is trained, on approximately 1000 query-map image pairs, it is able to improve the performance of vehicle detection when combined with an existing off-the-shelf vehicle detector. We demonstrate our approach using standard datasets across two cities (Oxford and Zurich) under different settings of train-test separation of map-query traverse pairs. We further emphasize the performance gains of our approach against alternative design choices and show that VPR suffices for the task, eliminating the need for precise ground truth localization.

翻译：能否通过知道自身位置来辅助感知周围环境中的物体，尤其是在恶劣天气和光照条件下？本文研究能否利用先验地图辅助场景中动态目标检测，而无需使用三维地图或像素级地图-查询对应关系。我们提出一种算法，该算法对初始候选目标检测结果进行精炼，通过先验地图生成高度精确的检测子集。首先利用视觉位置识别（VPR）为给定查询图像检索参考地图图像，随后采用二元分类神经网络比较查询与地图图像区域，以验证查询检测结果。在约1000对查询-地图图像对上完成分类网络训练后，该网络与现有商用车辆检测器结合可提升车辆检测性能。我们使用两个城市（牛津与苏黎世）的标准数据集，在不同地图-查询遍历对训练-测试分离设置下验证方法有效性。进一步通过与替代设计方案对比，强调本方法的性能增益，并证明视觉位置识别足以胜任该任务，从而无需精确的真值定位。