Determining motion parameters for scenes with translation and rotation

Qualitative Understanding of Scene Dynamics for Mobile Robots

In this paper, we present a new approach to analyzing motion sequences as they are observed from a mobile robot operating in a dynamic environment. In particular, we address the problems of (a) estimating the robot's egomotion, (b) recon structing the 3D scene structure, and (c) evaluating the mo tion of individual objects from a sequence of monocular images. Our approach consists of a two-stage process starting from given sets of displacement vectors between distinct image features in successive frames. First, the robot's egomo tion is computed in terms of rotations and the direction of translation. To cope with the problems of noise, we have extended the concept of the Focus of Expansion (FOE) by computing a Fuzzy FOE, which defines an image region rather than a single point. In the second stage, a 3D scene model is constructed by analyzing the movements and posi tions of image features relative to each other and relative to the Fuzzy FOE. Using a mainly qualitative strategy of rea soning and modeling, multiple scene interpretations are pursued simultaneously. This second stage allows the deter mination of moving objects in the scene. Results of this ap proach applied to a real image sequence are presented.

Eliminating false matches for the projective registration of free-form surfaces with small translational motions

In this paper, we make a detailed study of two rigid-motion constraints. The importance of these two constraints is twofold: first, they reveal the inherent relationship between the three-dimensional-two-dimensional (3-D-2-D) point correspondences and the motion parameters of interest; second, they can be used to measure the traditional ICP criterion established point match qualities based on which different point matches can be compared and relatively good point matches can be selected for motion-parameter update in the projective registration of free-form surfaces subject to small translational motions. The experimental results based on both synthetic data and real images have shown that the rigid motion constraints are powerful in evaluating the possible 3-D-2-D point matches established by the traditional ICP criterion, thus achieving encouraging projective registration results.

Determining three-dimensional motion and structure from optical flow generated by several moving objects

A new approach for the interpretation of optical flow fields is presented. The flow field, which can be produced by a sensor moving through an environment with several independently moving, rigid objects, is allowed to be sparse, noisy, and partially incorrect. The approach is based on two main stages. In the first stage, the flow field is partitioned into connected segments of flow vectors, where each segment is consistent with a rigid motion of a roughly planar surface. In the second stage, segments are grouped under the hypothesis that they are induced by a single, rigidly moving object. Each hypothesis is tested by searching for three-dimensional (3-D) motion parameters which are compatible with all the segments in the corresponding group. Once the motion parameters are recovered, the relative environmental depth can be estimated as well. Experiments based on real and simulated data are presented.

Determining motion parameters for scenes with translation and rotation

A study of methods that determine the rotation parameters of a camera moving through synthetic and real scenes is conducted. Algorithms that combine ideas of Jain and Prazdny, using hypothesizeand-verify paradigm, are developed to find translational and rotational parameters. An argument is made for using hypothesized motion parameters rather than relaxation labeling to find correspondence. Some work with real scenes shows the difficulties introduced by noise, the lack of resolution, and the need for better low-level techniques.