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An effective ground plane extraction using depth map estimation from a Kinect device

This paper presents a new approach to extract ground planes from a depth map which is provided by Kinect. The proposed system applies an robust algorithm to calculate the depth gradient maps (GDM) with high accuracy. Then the correct partition provides a set of candidates for the selection of ground. Last, it uses an efficient filter to find out the truth ground planes. The results prove the certainty of the algorithm in both cases consisting of the perfect data and actual scenes. For first case, the percentage of truth ground pixel detection R1 is common over 90%. | Journal of Science & Technology 123 (2017) 019-025 An Effective Ground Plane Extraction using Depth Map Estimation from a Kinect Device Dang Khanh Hoa*, Pham The Cuong, Nguyen Tien Dzung Hanoi University of Science and Technology, No. 1, Dai Co Viet, Hai Ba Trung, Hanoi, Viet Nam Received: August 24, 2016; Accepted: November 03, 2017 Abstract This paper presents a new approach to extract ground planes from a depth map which is provided by Kinect. The proposed system applies an robust algorithm to calculate the depth gradient maps (GDM) with high accuracy. Then the correct partition provides a set of candidates for the selection of ground. Last, it uses an efficient filter to find out the truth ground planes. The results prove the certainty of the algorithm in both cases consisting of the perfect data and actual scenes. For first case, the percentage of truth ground pixel detection R1 is common over 90%. The percentage of incorrect ground pixels detection R2 is lower than 5%. For the second case, the process of implementing the proposed algorithm on a depth map from Kinect also is compared with RANSAC algorithm and Enhanced V-Disparity algorithm. The result demonstrates that the proposed method’s R1 is usually greater than RANSAC method and V-Disparity method 2%, while R2 of the proposed method is less than half of R2 of the compared methods, respectively. The experimental results show the ability to respond in real time when this work is deployed as a stereo vision-based navigation system. Keywords: Depth map, gradient, ground plane, Kinect, vehicle 1. Introduction * cloud data [6-7]. An image homograph method has been demonstrated in [8-9] with an simple calculation. But this method’ results is only suitable for environments with non-complex ground. The works using a data stream collected from a single camera are quite outstanding [10-12]. They only demand a simple image acquisition system without depth data. The proposed process take a 2D color image sequence