The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 819–825, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-819-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 819–825, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-819-2019

  05 Jun 2019

05 Jun 2019

CONSTRUCTION OF OBSTACLE ELEMENT MAP BASED ON INDOOR SCENE RECOGNITION

F. Li, H. Wang, P. H. Akwensi, and Z. Kang F. Li et al.
  • Department of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, 100083, China

Keywords: PointNet, Point cloud, Indoor scenes, Markov Random Field, Semantic recognition, Obstacle element map

Abstract. Route planning and navigation in indoor space have become a hot topic recently. To accomplish this task, a map and a real-time detection system are needed. Due to Lidar systems’ high efficiency in data acquisition, Lidar sensors have become an indispensable part of an object detection system. In this paper, we use Lidar points to generate obstacle maps. The obstacle maps can be used as a reference for route planning and navigation. To identify single objects more precisely, a deep network combined PointNet with Markov Random Field (MRF) is designed in our work to classify Lidar points. Then, single objects are segmented by using the Euclidean clustering method. After that, the prior rules and derived criteria we summarized from large amount images are used to determine objects’ kind between Influence Movement Obstacles (IMO) and Non-Influence Movement Obstacles (N-IMO). Finally, objects are projected into a 2D plane to generate obstacle maps. To evaluate the performance of our method, experiments were performed on the S3DIS dataset of Stanford University. The results show that our method greatly improves the overall accuracy compared to the original PointNet model, and can generate high-quality obstacle maps.