Sub-pixel depth accuracy with a time of flight sensor using multimodal Gaussian analysis

Pixel array based 3D range sensors like time of flight sensors (e.g. Swiss-ranger) are commonly used for spatial mapping. Analogous to a laser range finder beam, each pixel measures distances within its conical field of view. However, unlike laser range finders, these sensors use incoherent visible or near visible light for range measurement. This, combined with their relatively long maximum ranges, means that the convenient thin beam assumption can no longer be used to update an occupancy grid map using a probabilistic sensor model. We present an analysis of a scenario where a pixel beam is intersected by more than one object and where a unimodal probabilistic distribution assumption causes spurious objects to appear in the detected scene. This adversely affects path-planning based on such maps because perfectly good escape routes are blocked off. A two step multimodal Gaussian mixture model based procedure is presented which is able to detect multiple obstacles per pixel and hence ameliorate the problem. The results of experiments done using a time of flight based sensor are presented.