Dynamic integration of height maps into a 3D world representation from range image sequences

Integration of 21/2D sketches obtained at different observation stations into a consistent world (or object) representation is one of the central issues in computer vision and robotics. The resolution and accuracy of 21/2D sketches may be different from one view point to another, and inconsistent data between different observations may occur. This article presents an approach to building a spatiotemporal representation of dynamic scenes including moving objects from a sequence of range images taken by a moving observer. A range image is transformed into a height-map representation, which is segmented into the ground plane and objects on it. In order to capture the resolution and accuracy of the range information and the consistency of the height information between different height maps, we define a reliability measure of the height information for each bucket on the height map. Using this reliability, the system finds the correspondences of both static and moving objects between different observations, and successively refines the height information and its reliability with newly acquired data, dealing with inconsistent data. Final representation of the integrated height map consists of the time stamp of the last observation, region labels of static and moving objects and their spatiotemporal properties such as height information, its reliability, and the velocities of both the observer and independently moving objects. We applied the method to road scenes physically simulated by landscape toy models and show the experimental results.

[1]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[2]  Minoru Asada,et al.  Interpretation And Integration Of Height Maps From A Range Image Sequence , 1989, Proceedings. IEEE/RSJ International Workshop on Intelligent Robots and Systems '. (IROS '89) 'The Autonomous Mobile Robots and Its Applications.

[3]  Matthew Turk,et al.  Video road-following for the autonomous land vehicle , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[4]  Robert C. Bolles,et al.  An evolutionary approach to constructing object descriptions , 1991 .

[5]  Olivier D. Faugeras,et al.  Building, Registrating, and Fusing Noisy Visual Maps , 1988, Int. J. Robotics Res..

[6]  Larry H. Matthies,et al.  Kalman filter-based algorithms for estimating depth from image sequences , 1989, International Journal of Computer Vision.

[7]  J. G. Harris,et al.  Knowledge-based vision technology overview for obstacle detection and avoidance , 1989 .

[8]  Martial Hebert,et al.  Vision and navigation for the Carnegie-Mellon Navlab , 1988 .

[9]  M. Nagao,et al.  Edge preserving smoothing , 1979 .

[10]  Larry S. Davis,et al.  A visual navigation system , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[11]  Robert C. Bolles,et al.  Epipolar-plane image analysis: An approach to determining structure from motion , 1987, International Journal of Computer Vision.

[12]  Larry S. Davis,et al.  Production of Dense Range Images with the CVL Light-Stripe Range Scanner. , 1988 .