Navigation system for a mobile robot incorporating trinocular vision for range imaging

This research focuses on the development of software for the navigation of a mobile robot. The software developed to control the robot uses sensory data obtained from ultra sound, infra red and tactile sensors, along with depth maps using trinocular vision. Robot navigation programs were written to navigate the robot and were tested in a simulated environment as well as the real world. Data from the various sensors was read and successfully utilized in the control of the robot motion. Software was developed to obtain the range and bearing of the closest obstacle in sight using the trinocular vision system. An operator supervised navigation system was also developed that enabled the navigation of the robot based on the inference from the camera images

Traffic scene awareness for intelligent vehicles using ConvNets and stereo vision

In this paper, we propose an efficient approach to perform recognition and 3D localization of dynamic objects on images from a stereo camera, with the goal of gaining insight into traffic scenes in urban and road environments. We rely on a deep learning framework able to simultaneously identify a broad range of entities, such as vehicles, pedestrians or cyclists, with a frame rate compatible with the strict requirements of onboard automotive applications. Stereo information is later introduced to enrich the knowledge about the objects with geometrical information. The results demonstrate the capabilities of the perception system for a wide variety of situations, thus providing valuable information for a higher-level understanding of the traffic situation

On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey

Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future.Comment: Accepted to TPAMI. Paper version of our CVPR 2019 tutorial: "Learning-based depth estimation from stereo and monocular images: successes, limitations and future challenges" (https://sites.google.com/view/cvpr-2019-depth-from-image/home

A computationally efficient stereo vision algorithm for adaptive cruise control

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 55-56).by Jason Robert Bergendahl.M.S