We address the problem of building a map of the environment utilizing sensory depth information obtained from multiple viewpoints. The desired representation of the environment is in the form of a finite-resolution three-dimensional grid of voxels. Each voxel within the grid is assigned a binary value corresponding to its occupancy state. We present an approach for multi-sensory depth information assimilation based on the Dempster-Shafer theory for evidential reasoning. This approach provides a mechanism to explicitly model ignorance which is desirable when dealing with an unknown environment. We present results obtained from this approach on a laboratory stereo sequence and contrast these with results obtained from the traditional Bayesian approach.>

An approach for incremental refinement of disparity maps obtained from a dynamic stereo sequence of a static scene is presented. The approach has been implemented using a binocular stereo vision system mounted on a mobile robot. A robust least median of squares based algorithm is given for recovering the camera motion between successive viewpoints, which provides a self-calibration mechanism. The recovered motion is utilized for recursive disparity prediction and refinement using a robust Kalman filter model.>

Dynamic stereo is useful for constructing a complete map of the environment as only a portion of the actual environment is visible from each viewpoint. In addition, there is usually an overlap between the portions of the environment visible from two successive viewpoints. It is then feasible to utilize a prediction-verification approach to combine the individual depth estimates of features visible from both viewpoints to obtain a more accurate estimate. A fundamental requirement for such an approach to be used is accurate knowledge of the camera motion between the two viewpoints. A robust least median of squares (LMS)-based algorithm to recover this motion which provides a self-calibration mechanism is presented. The recovered motion is utilized for recursive disparity prediction and refinement using a robustified Kalman filter formulation. Results are presented for a laboratory stereo sequence.>

A nonlinear least-squares optimization technique is proposed which uses the Levenberg-Marquardt method and estimates the motion and structure parameters to a global scale factor by minimizing an objective function. This objective function is the mean-square difference between the measured coordinates of feature points in the image plane and the coordinates predicted from the current state estimate. In comparison to existing approaches, this technique converges faster and yields better estimates. A recursive version of this algorithm is developed using the block approach. This algorithm is shown to also track eventful motion effectively. The performance of the proposed technique on real image sequences is also presented. Some performance results are indicated to illustrate the efficacy of this approach.>