Obstacle Detection Approach Research Articles

A neural approach for obstacle detection with a linear stereoscopic sensor

A neural implementation for achieving real-time obstacle detection in front of a moving vehicle using a linear stereoscopic sensor is presented. The key problem is the so-called “correspondence problem” which consists in matching features in two stereo images that are projections of the same physical entity in the three-dimensional world. In our approach, the set of edge points extracted from each linear image is first split into two classes. Within each of these classes, the matching problem is turned into an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. The optimization problem is then performed thanks to an analog Hopfield neural network. The preliminary discrimination of the edge points allows us to implement the matching process as two networks running in parallel. Experimental results are presented to demonstrate the effectiveness of the approach for 3-D reconstruction in real traffic conditions.

A neural matching algorithm for 3-D reconstruction from stereo pairs of linear images

In this paper, we propose a neural approach for obstacle detection in front of moving cars, using linear stereo vision. The key problem is the so-called “correspondence problem” which consists in matching features extracted from two images that are projections of the same entity in the three-dimensional world. The linear stereo correspondence problem is first formulated as an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. The optimization problem is then performed by means of a Hopfield neural network. Experimental results, using real stereo images, demonstrate the effectiveness of the method.

Vision‐based obstacle detection for rotorcraft flight

AbstractThe ability of rotorcraft to fly at low altitude is hindered by the high pilot workload required to avoid obstacles. The development of automation tools that can detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles would significantly reduce pilot workload and increase safety. This article describes an obstacle detection approach based on feature tracking and recursive range estimation that takes into account the characteristics of rotorcraft flight. The merits and weaknesses of the approach are discussed using image sequences from the laboratory and from flight. © 1992 John Wiley & Sons, Inc.

Computer vision techniques for rotorcraft low-altitude flight

A description is given of research that applies techniques from computer vision to automation of rotorcraft navigation, especially when flying low to avoid detection by an enemy. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle detection approach can be used as obstacle data for the obstacle avoidance in an automatic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data, however, presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate, flight database and by preprocessing of currently available flight imagery. Some comments are made on future work and how research in this area relates to the guidance of other autonomous vehicles.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>