3D Semantic Mapping: a Benchmark and Baseline Method. Issue 20 (2021)

• Record Type: Journal Article
• Authors: Zhang, Guoxiang; Wang, Peng; Chen, Ning; Chen, YangQuan
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 20(2021)
• Page Start: 820
• Page End: 825
• Publication Date: 2021
• Subjects: semantic mapping -- 3D perception -- autonomous driving; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.11.273
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, a simple and effective real-time 3D semantic mapping method is proposed. The proposed method take per-frame bounding box detections and sensor (camera) extrinsic transformation estimates as inputs and produces a set of static 3D bounding boxes in world coordinate system as 3D semantic mapping results. Each object has a Kalman filter as its state estimator and intersection over union of 3D bounding boxes is used for data association. To evaluate the proposed method, a new benchmark is derived from the KITTI object tracking evaluation. Ground-truth semantic maps are constructed based on oxts data and labeled 3D bounding boxes of KITTI. Three novel semantic map-centered metrics: DAOD, AAOD, and PRVO are proposed. Experiments are conducted to evaluate the proposed method. Experiments show that the metric and benchmarking dataset can serve as a new benchmark platform for easier comparison of new methods.