Stereo for robots: Quantitative evaluation of efficient and low-memory dense stereo algorithms

Despite the significant number of stereo vision algorithms proposed in literature in the last decade, most proposals are notably computationally demanding and/or memory hungry so that it is unfeasible to employ them in application scenarios requiring real-time or near real-time processing on platforms with limited resources such as embedded devices. In this paper, we have selected the subset of proposals that appears more suited to the above requirements and, since literature lacks a proper comparison between these methods, we propose a quantitative experimental evaluation aimed at highlighting the best performing approach under the two criteria of accuracy and efficiency. The evaluation is performed on a standard benchmark dataset as well as on a novel dataset, acquired by means of an active technique, characterized by realistic working conditions.