Trinocular Stereovision for Robotics

An approach to building a three-dimensional description of the environment of a robot using three cameras is presented. The main advantages of trinocular versus binocular stereo are simplicity, reliability, and accuracy. It is believed that these advantages make trinocular stereo vision of practical use for many robotics applications. The technique has been successfully applied to several indoor and industrial scenes. Experimental results are presented and discusse

A Surface Relief Meter Based on Trinocular Vision

The concept for the relief meter being developed, appears to function well, when used with the artificial images. The described matching criterion leads to high matching percentages, and accurate results. The percentage of mismatches is reduced to practically zero for the tested scenes. Future work will involve evaluation of the algorithm with real agricultural scenes (soil images) and implementation of special hardware for fast execution of the algorith

Learning monocular depth estimation with unsupervised trinocular assumptions

Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing. CNNs led to considerable improvements in this field, and recent trends replaced the need for ground-truth labels with geometry-guided image reconstruction signals enabling unsupervised training. Currently, for this purpose, state-of-the-art techniques rely on images acquired with a binocular stereo rig to predict inverse depth (i.e., disparity) according to the aforementioned supervision principle. However, these methods suffer from well-known problems near occlusions, left image border, etc inherited from the stereo setup. Therefore, in this paper, we tackle these issues by moving to a trinocular domain for training. Assuming the central image as the reference, we train a CNN to infer disparity representations pairing such image with frames on its left and right side. This strategy allows obtaining depth maps not affected by typical stereo artifacts. Moreover, being trinocular datasets seldom available, we introduce a novel interleaved training procedure enabling to enforce the trinocular assumption outlined from current binocular datasets. Exhaustive experimental results on the KITTI dataset confirm that our proposal outperforms state-of-the-art methods for unsupervised monocular depth estimation trained on binocular stereo pairs as well as any known methods relying on other cues.Comment: 14 pages, 7 figures, 4 tables. Accepted to 3DV 201