Real-time adaptive obstacle detection based on an image database

In this paper, an innovative method for the detection and avoidance of obstacles is presented. This is based on image registration techniques. The aim of this method is the detection of the possible obstacles that could be in the route where VERDINO (an autonomous electrical vehicle which is going to work in the surroundings of a bioclimatic urbanization as part of the SAGENIA project) navigates. The obstacle detection is one of the most critical parts of the prototype. It is responsible for the detection and later avoidance of the pedestrians, cars, etc. that can damage it or be damaged by the prototype. The algorithm is able to work in real time, with good detection rates and a fast response. It also includes a dynamical database that will allow the vehicle to learn adaptively the current state of the environment, rejecting old images. With this, some of the typical problems related to the image registration techniques are removed. Some examples and results are provided, corroborating the good behavior of the algorithm, which has been tested in simulated and real conditions. The method is also applicable to other tasks, like surveillance for non-stationery cameras or other applications over very different kinds of images.

► A different way of detecting obstacles for autonomous vehicles has been achieved. ► The system works under the established real time constraints. ► Good obstacle detection rate. ► A dynamic database ensures the durability of the system. ► Integration of diverse sensors solves some of the problems inherent to the method.