3D Map Registration using Vision/Laser and Inertial Sensing

A point cloud registration method is proposed in this article, and experimental results are presented for long threedimensional map sequences obtained from a moving observer. In vision based systems used in mobile robotics the perception of self-motion and the structure of the environment is essential. Inertial and earth field magnetic pose sensors can provide valuable data about camera ego-motion, as well as absolute references for the orientation of scene structure and features. In this work we explore the fusion of inertial and magnetic sensor data with range sensing devices. Orientation measurements from the inertial system are used to rotate the obtained 3D maps into a common orientation, compensating the rotational movement. Then, image correspondences are used to find the remaining translation. Results are presented using both a stereo camera and a laser range finder as the ranging device. The laser range finder also needs a single camera to stabilish pixel correspondence. The article overviews the camera-inertial and camera-laser calibration processes used. The map registration approach is presented and validated with experimental results on indoor and outdoor environments.

[1]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[2]  Roland Siegwart,et al.  Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Kurt Konolige,et al.  Small Vision Systems: Hardware and Implementation , 1998 .

[4]  Jorge Dias,et al.  Inertial Sensed Ego-motion for 3D Vision , 2004, J. Field Robotics.

[5]  Jorge Lobo,et al.  Bioinspired Visuovestibular Artificial Perception System for Independent Motion Segmentation , 2006 .

[6]  Jorge Dias,et al.  Relative Pose Calibration Between Visual and Inertial Sensors , 2007, Int. J. Robotics Res..

[7]  Jorge Dias,et al.  Vision and Inertial Sensor Cooperation Using Gravity as a Vertical Reference , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jonathan M. Garibaldi,et al.  Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[9]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[10]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[11]  Stereo Vision 3 D Map Registration for Airships using Vision-Inertial Sensing , 2006 .

[12]  Wolfram Burgard,et al.  Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Larry H. Matthies,et al.  Error modeling in stereo navigation , 1986, IEEE J. Robotics Autom..

[14]  Kazunori Ohno,et al.  Dense 3D map building based on LRF data and color image fusion , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.