Map-Driven Image Interpretation by Associative Model Indexing

The problem of integrating territorial information within a multisensor vision system for autonomous-vehicle control is addressed. Environmental information is used to improve recognition results and to locate a vehicle's position in the coordinate reference frame of a map. To this end, a hypothesis-and-test search mechanism has been developed, which is based on an associative phase and a symbolic. In particular, an associative memory is first used to address the possible territorial area where the scene under examination may have been acquired. This guess is then verified by a symbolic recognition system using a model-driven strategy. The integration of multiple information sources is basic to obtain an accurate recognition of 3D outdoor scenes, especially when controlling an autonomous vehicle. In this paper, we address the problem of integrating territorial information into a multisensor vision system for autonomous driving. A set of synthetic images, representing significant viewframes reconstructed from an a-priori fixed route on a territorial map are first stored in an associative memory [9]. This process represents the training phase of the associative memory. Images acquired by a multisensor set-up are then processed by the associative memory in order to produce an estimation of the vehicle position inside the map reference frame. This strategy makes it possible to arrange the search space, in such a way as to avoid the search in the whole model space, thus obtaining a better computational performance. This initial guess gives a position estimation which is then verified by the recognition system by looking for objects associated with the viewframe. This process is performed at a high abstraction level, and consists in an expectation-driven search starting from symbolic object descriptions and using a version of a distributed blackboard system for recognition [4], where a module devoted to scene analysis has been inserted. The paper is organized into in four sections. Section I1 deals with a general formulation of the problem, pointing out the characteristic of the sensors employed . Section Ill contains a brief review of associative memory techniques, and Section IV contains a description of the model here employed and it reports preliminary results obtained on a set of real images and on the related territorial map. terrain map are transformed so that they can be fused with data acquired with a TV-camera, . Then, the recognition process performed at the symbolic level is described. 21 cartographi virtual sensor A topologic map (TM) representing a scenario through which an autonomous vehicle can ride provides useful information to be used by a multisensor recognition system. Two intermediate steps have to be performed to obtain a representation of the information contained in the map that it can be compared directly with data provided by visual sensors: first, a 3D model of the environment must be obtained; then an observation model must be provided allowing the system to simulate the acquisition of data as similar as possible to those coming from the visual sensor.