H.K. Nakamura

T. Umetani

Y. Tamura

National Institute for Fusion Science (NIFS-Repository)

Introduction A localization system for detecting auser’s position is useful to provide current location infor-mation. The Global Positioning System (GPS) is gen-erally used to obtain such information; however, it can-not be used inside buildings. To overcome this prob-lem, many studies have considered using machine visionwith cameras. Vision-based localization methods with1) and without artiﬁcial markers 2) have been proposed.However, measuring depth value with only a camera isgenerally inaccurate. We propose a localization systemusing Microsoft Kinect, which has both a red-green-blue(RGB) camera and a depth camera.Processing Procesure Figure 1 shows the procedureof the proposed system. First, the RGB and depth im-ages are calibrated. Then natural feature points are ex-plored, and base points, which are feature points with3D location data, are selected and stored. Finally, the3D location of the system (Kinect) is calculated fromthe base points. Base points are tracked with templatematching and their locations are updated.Result We used this system to estimate the dimen-sions of a room and a corridor. The room size is about7.5 m × 6.5 m. The Kinect was located on the markedpoint in Fig. 2 (a) and rotated 360◦ on a turntable. Fig-ure 2 (b) shows the result of estimation in the corridor.The width of the corridor is about 1.7 m. The estimateswere repeated 10 times in the room. The average mea-surement error was 31 cm (± 6 cm standard deviation)in the x-direction, 17 cm (± 3 cm) in the y-direction,and 41 cm (± 12 cm) in the z-direction.Conclusion We proposed a localization system usingthe Microsoft Kinect sensor. We applied this system toa room and a corridor. The estimation results had ahorizontal error of around 40 cm and a vertical error ofabout 20 cm.1) Naimark, L. et al. : Proc. IEEE/ACM Int. Symp onMixed and Augmented Reality (2002) 27.2) Oe, M., et al. : Proc. 14th Scandinavian Conf onImage Analysis (2005) 171.RGB image captureCalibrate the image area size between depth image and RGB image.Detect natural feature points Find base pointsUpdate basing pointsEstimate extrinsic camera parametersUpdate 3D data and camera positionDepth image captureTrack base pointsFig. 1: Processing procedure.(a)(b)Fig. 2: 3D reconstruction result in the room (a) and inthe corridor (b).403§32. Localization with Microsoft Kinect Using Natural Features and Depth DataTamura, Y., Umetani, T. (Konan Univ.), Nakamura, H.

Localization with Microsoft Kinect Using Natural Features and Depth Data

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