Object Detection and Mapping with Bounding Box Constraints

In this paper, we present a three-dimensional object detection method for a single image and an object-based localization and mapping system. For 3D object detection, we firstly generate high-quality cuboid candidates by sampling object rotation and dimension. Then, the translation of each candidate is estimated in a closed form solution with camera projection function and bounding box constraints. Finally, all candidates are projected into the image, scored and selected based on the alignment with detected lines. To overcome object detection accuracy issues, the results are improved by multi-view optimization. Besides, objects can provide geometry constraints and semantic information to improve camera pose estimation and monocular drift. A point-object SLAM system is formulated to jointly optimize the poses of camera, objects and points. We evaluate our object detection method on objects from the KITTI, the SUN RGB-D and a self collected dataset. The results show that our method outperforms existing approaches. The point-cuboid SLAM experiments on the TUM RGB-D, ICL-NUIM and our self collected dataset show that our algorithm can improve both camera localization accuracy and 3D object detection accuracy.