A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the f...

Visual processing is very important for robot navigation. It has been demonstrated that many complex operations, which deserve an intelligent behaviour, can be performed relying only on reflexes to visual stimuli. In this framework the detection of corridors of free space along the robot trajectory is certainly a very important capability to safely navigate. Stereo vision and motion parallax can be used as cues to infer scene structure and determine free space areas. In this paper we propose a cooperative schema in which binocular disparity, computed on several stereo images over time, is combined with optical flow from the same sequence to obtain a relativedepth map of the scene. Both time-to-impact and depth scaled by the distance of the camera from the fixation point in space are considered as good, relative measurements which are based on the viewer (but centered on the environment). Simple relations are defined which combine disparity and optical flow for relative-depth estimates....

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A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles. CR Categories: I.3.5 [Computer Graphics] Computational Geometry and Object Modeling Additional keywords: Surface fitting, three-dimensional shape recovery, range image integration, isosurface extraction 1