Recent research in image sensors has produced cameras with very large fields of view. An area of computer vision research which will benefit from this technology is the computation of camera motion (ego-motion) from a sequence of images. Traditional cameras suffer from the problem that the direction of translation may lie outside of the field of view, making the computation of camera motion sensitive to noise. In this paper, we present a method for the recovery of ego-motion using omnidirectional cameras. Noting the relationship between spherical projection and wide-angle imaging devices, we propose mapping the image velocity vectors to a sphere, using the Jacobian of the transformation between the projection model of the camera and spherical projection. Once the velocity vectors are mapped to a sphere, we show how existing ego-motion algorithms can be applied and present some experimental results. These results demonstrate the ability to compute egomotion with omnidirectional cameras....

In homing tasks, the goal is often not marked by visible objects but must be inferred from the spatial relation to the visual cues in the surrounding scene. The exact computation of the goal direction would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. However, if prior assumptions about typical distance distributions are used, a snapshot taken at the goal suffices to compute the goal direction from the current view. We show that most existing approaches to scene-based homing implicitly assume an isotropic landmark distribution. As an alternative, we propose a homing scheme that uses parameterized displacement fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that both approximations do not prevent the schemes from approaching the goal with arbitrary accuracy, but lead to different...

We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach. Introduction 1 To survive in unpredictable and sometimes hostile environments animals have developed powerful strategies to find back to their shelter or to a previously visited food source. Successful navigation can already be achieved using simple mechanisms such as association of landmarks with movements (Wehner et al. 1996) or tracking of environmental features (Collett 1996). To understand more complex forms of spatial behaviour like finding shortcuts, however, we have to go beyond reactive control strategies, towards systems with internal states. In as far as they ...

The paradigm for image based localization using panoramic images is elaborated. Panoramic images provide complete views of an environment and their information content does not change if a panoramic camera is rotated. The "zero phase representation" of cylindrical panoramic images, an example of a rotation invariant representation, is constructed for the class of images which have non-zero first harmonic in column direction. It is an invariant and fully discriminative representation. The zero phase representation is demonstrated by an experiment with real data and it is shown that the alternative autocorrelation representation is outperformed.

The ability to control egomotion using low resolution peripheral vision is crucial for enabling a small high resolution fovea to attend to features that require detailed examination. The beebot demonstrates the ability to use low resolution motion vision over large fields of view to steer between obstacles. The system uses the maximum flow observed in left and right peripheral visual fields to indicate obstacle proximity. Each peripheral field constitutes one-third of a wide-angle lens. The left and right proximities are compared to steer through the gap. Negative feedback control of steering is able to tolerate inaccuracies in the signal estimation. This interpretation of the flows is based on the assumption that the camera is translating along the gaze vector. This condition is maintained under egomotion by active gaze stabilization. Head rotation is countered by eye rotation, and gaze is returned to the heading by rapid camera movements when necessary. The low cost of such basic nav...

This paper explores the application of optical flow techniques for visual homing. The performance of five different flow techniques and a reference method is analysed based on image collections from three different indoor environments. We show that block matching, two simple variants of block matching and two even simpler differential techniques produce robust homing behaviour, despite the simplicity of the matched features. Our analysis reveals that visual homing can succeed even in the presence of many incorrect feature correspondences, and that low-frequency features are sufficient for homing. In particular, the successful application of differential methods opens new vistas on the visual homing problem, both as plausible and parsimonious models of visual insect navigation, and as a starting point for novel robot navigation methods

Computing a camera's ego-motion from an image sequence is easier to accomplish when a spherical retina is used, as opposed to a standard retinal plane. On a spherical field of view both the focus of expansion and contraction are visible, whereas for a planar retina that is not necessarily the case.

In this paper we present a general methodology for designing mirrors of catadioptric omnidirectional sensors encompassing linear projection properties, the so called constant resolution cameras. The linearity is stated between 3D distances (or angles) and pixel coordinates.

Determining the structure of the world and the motion of the observer from image changes has been a central problem in computer vision for over fifteen years. Since the early work on Structure from Motion (SFM) by Longuet-- Higgins [4] and Pradzny [6], several techniques have been developed to compute the motion of the camera, the shape of moving objects, or distances to points in the world. However, the image changes are non--linearly related to camera motion and distances to points in the world. Thus, solving the problem typically requires non--linear optimization techniques that can be unstable or computationally inefficient. Linear algorithms are preferable since they are computationally advantageous, and since linear estimation is much better understood than non--linear estimation. Our paper describes an unbiased, completely linear algorithm for Structure--from--Motion. This work is similar to that of Jepson & Heeger [3] except that we employ spherical projection. The use of a sph...

. Recent biological evidence suggests that position and orientation can be estimated from an adequately compressed set of environment snapshots and their relationships. In this paper we present a pure appearance-based localisation method using an eigenspace representation of panoramic images. We first review several types of rotational invariant representation of panoramic images in terms of their efficiency for an eigenspace-based localisation problem. Then, for each set of images an eigenspace from 25 location snapshots is built and analyzed. We evaluated simple localisation of images not included in the training set. The results show good prospects for the panoramic eigenspace approach. 1 Introduction It is well known that a large number of animal species uses predominantly visual information to navigate in space. Most animals use visual information in combination with odometry, but in special cases, such as moving in the air or underwater, this is not possible. Several m...