Expectation-based, Multi-focal, Saccadic (EMS-) Vision. (A System for Understanding Dynamic Scenes Observed from a Moving Platform)

Thursday, November 16, 2000 - 3:30pm - 4:30pm
Keller 3-180
Ernst Dickmanns (Universität (Hochschule) der Bundeswehr München)
Expectation-based, Multi-focal, Saccadic (EMS-) Vision has been designed to cope with many different aspects of mission performance for a variety of vehicles. A wide field of view (f.o.v., > ~100°) nearby allows to avoid moving obstacles at slow speed and to negotiate tight curves. Trinocular stereo in a small central f.o.v. yields good depth estimations in the near range with one single well recognizable feature. Active gaze control allows to shift this f.o.v. to where it is needed, and to inertially stabilize the viewing direction for eliminating motion blur. Ego-motion under strong perturbat ions is determined by inertial/visual data fusion taking advantage of spatio-temporal models on differ ential and integral scales.

Scene representation is done in a dynamic scene tree exploiting homogeneous coordinate transformations like in computer graphics; however, in computer vision many of the entries into the transformation matrices and the generic object models are the unknowns of the problem. In the 4 -D approach developed on the basis of the extended Kalman filtering, these unknowns are determined by an initial (daring) guess and consecutive recursive improvements by prediction error feedback exploiting rich first order approximations (in 3-D space including perspective mapping) through Jacobian matrices for each object/sensor-pair. The Dynamic Object dataBase (DOB) [containing the scene tree representation among other knowledge components about the vehicle status] is the central layer for separating the 'Systems-Engineering' lower part of the overall cognitive system from the more 'Artificial Intelligence'-oriented upper part with state chart representations. On the higher levels, the situation comprising several objects and the own intentions (goals) is assessed and behavioral decisions are taken in the mission context. Here, knowledge about the effects of maneuvers and of the application of feedback control laws i s available. Actual maneuver performance and control computations are done on the lower levels with dedicated processors in the distributed overall system (about a dozen processors). Experimental results in fully autonomous road vehicle guidance with the test veh icles 'VaMoRs' (a 5-ton van, maneuvering on minor road networks) and `VaMP' (a Mercedes 500 SEL, displaying hybrid adaptive cruise control on highways) will be shown.