A map of occupied and free space in a robot's environment is a common prerequisite for navigational tasks. Although the first methods for occupancy mapping relied on a 2D grid representation, 3D volumetric approaches are becoming increasingly popular. In this paper we present a new volumetric mapping approach that is based on the OctoMap method. We designed this method to be more robust against measurement errors, in particular against high temporally or spatially correlated errors usually received from a stereo vision system. For this purpose, we define a probability measure that a voxel is currently visible. An update of a voxel's occupancy probability then happens with respect to this visibility probability, allowing us to neglect measurements for voxels that are actually unobservable. Finally, we model the depth error of a stereo vision sensor, and take care of this error when performing a map update. By evaluation we show that our method produces maps with far less erroneous artifacts compared to OctoMap. Our maps also require less memory, and due to an optimized update reduction, our method is also faster than OctoMap when processing dense range measurement data.

Hardware platforms with limited processing power are often incapable of running dense stereo analysis algorithms at acceptable speed. Sparse algorithms provide an alternative but generally lack in accuracy. To overcome this predicament, we present an efficient sparse stereo analysis algorithm that applies a dense consistency check, leading to accurate matching results. We further improve matching accuracy by introducing a new feature detector based on FAST, which exhibits a less clustered feature distribution. The new feature detector leads to a superior performance of our stereo analysis algorithm. Performance evaluation shows that the proposed stereo matching system achieves processing rates above 200 frames per second on a commodity dual core CPU, and faster than video frame-rate processing on a low-performance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms.

In this study we examine three road-modeling methods, which we evaluate on seven stereo matching algorithms. The road-modeling methods we consider are a B-spline modeling technique based on region-growing and two versions of the popular v-disparity approach. The used stereo algorithms are variations or different parameterizations of belief propagation, graph cut and semi-global matching. We examine the performance of the possible combinations of modeling methods and stereo algorithms by comparing the deviation towards a reference road profile. Two evaluation sequences are used, of which one features switched on windscreen wipers that are visible in the the recorded imagery. Our findings are that the examined B-spline modeling method provides the best results in most cases. In terms of the modeled distance, belief propagation is the most suitable stereo matching method, which also performs best with the wiper sequence. Semi global matching achieves smaller model deviations, but with drastically reduced modeled distances.