We describe a method for estimating the translation and rotation between two subsequent poses of a moving robot from images taken with an omnidirectional vision system. This allows some form of visual odometry. We use two sorts of projections derived from the omnidirectional image. The rotation and translation direction are determined from panoramic projections. After that, a projection on a plane parallel to the ground is used to estimate the length of the translation vector. Experiments on real and simulated data are carried out.

In this paper, we present an efficient multi-baseline stereo algorithm for panoramic image data. We derive a parameterization of epipolar curves in terms of inverse depth. As a result, the search for image correspondences across multiple images can be performed efficiently. Furthermore, depth estimates are obtained directly, thus bypassing the need to perform explicit stereoscopic triangulation. We apply our method to obtain a three-dimensional reconstruction of an environment from a set of panoramic images. The images are acquired by a single omnidirectional vision sensor mounted on top of our mobile robot during navigation. Experimental results demonstrate the effectiveness of our approach.

We address the problem of recovering range information from a pair of images obtained from camera viewpoints whose relative poses are known (stereo vision). The images we deal with are cylindrical re-projections of images captured by an omnidirectional vision sensor. Images obtained by such a device have a relatively low spatial resolution in comparison to standard camera images. We analyze the sensitivity of range estimation from image correspondences with respect to errors caused by discretization. The analysis reveals the significance of obtaining sub-pixel accuracy for range estimation. We present our method and experimental results. The results indicate that high accuracy range estimates can be obtained using our method.

Feature extraction from robot sensor data is a standard way to deal with the high dimensionality and redundancy of such data. In order to get optimal task-relevant features, PCA must be replaced by a supervised projection method. In this paper we extend our previously proposed supervised linear feature extraction method (2000) in two ways: 1) the projection matrix is optimized simultaneously over all columns under the constraint of orthonormality; and 2) a Jacobi parametrization of the matrix allows the use of unconstrained nonlinear optimization algorithms. The new algorithm is more efficient and many times faster than the old version. We show experimental results in extracting features from panoramic images of a mobile robot. The results compare favorably to the PCA solutions.

In order to do useful things a mobile robot needs some sort of global information about the environment it is operating in. In the paper an approach is described where the global information is not cast in a model of the geometry of the environment but in a model of all sensory data of the robot. As a primary sensing system we use computer vision. The model gives a probability distribution over the learned locations given an observation. We developed an active vision strategy to increase the performance and tested the method on real image data from our robot.