Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 431-437, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-431-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
12 Sep 2017
AUTOMATIC RECOGNITION OF INDOOR NAVIGATION ELEMENTS FROM KINECT POINT CLOUDS
L. Zeng and Z. Kang School of Land Science and Technology, China University of Geoscience, Beijing China
Keywords: Indoor Navigation, Navigation Elements, Auto-recognition, Kinect, IndoorGML Abstract. This paper realizes automatically the navigating elements defined by indoorGML data standard – door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor – histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor – in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.
Conference paper (PDF, 1959 KB)


Citation: Zeng, L. and Kang, Z.: AUTOMATIC RECOGNITION OF INDOOR NAVIGATION ELEMENTS FROM KINECT POINT CLOUDS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 431-437, https://doi.org/10.5194/isprs-archives-XLII-2-W7-431-2017, 2017.

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