Obstacle fusion algorithms usually perform obstacle association and gating in order to improve the obstacle position if it was detected by multiple sensors. However, this strategy is not common in multi sensor occupancy grid fusion. Thus, the quality of the fused grid, in terms of obstacle position accuracy, largely depends on the sensor with the lowest accuracy. In this paper an efficient method to associate obstacles across sensor grids is proposed. Imprecise sensors are discounted locally in cells where a more accurate sensor, that detected the same obstacle, derived free space. Furthermore, fixed discount factors to optimize false negative and false positive rates are used. Because of its generic formulation with the covariance of each sensor grid, the method is scalable to any sensor setup. The quantitative evaluation with a highly precise navigation map shows an increased obstacle position accuracy compared to standard evidential occupancy grid fusion.

In this paper we describe a novel approach to model-based monocular vehicle tracking out of a moving vehicle using active vision. The designed algorithm can cope with cluttered color images, complex lighting conditions as well as partial occlusion of the leading vehicle and is able to detect and track a vehicle even within unstructured offroad environments. Thanks to the used 3D model which describes the characteristic vehicle geometry and appearance in terms of vertexes, edges and colored surfaces, no special visual markers are required. The knowledge of vehicle's geometry and appearance gained from the model are used within a particle filter to estimate the 6DoF position relative to the ego vehicle, thereby fusing edge as well as color information. We successfully use the proposed algorithm for pure vision based autonomous offroad convoy driving.

Three-dimensional ultrasound can be an effective imaging modality for image-guided interventions since it enables visualization of both the instruments and the tissue. For robotic applications, its realtime frame rates create the potential for image-based instrument tracking and servoing. These capabilities can enable improved instrument visualization, compensation for tissue motion as well as surgical task automation. Continuum robots, whose shape comprises a smooth curve along their length, are well suited for minimally invasive procedures. Existing techniques for ultrasound tracking, however, are limited to straight, laparoscopic-type instruments and thus are not applicable to continuum robot tracking. Toward the goal of developing tracking algorithms for continuum robots, this paper presents a method for detecting a robot comprised of a single constant curvature in a 3D ultrasound volume. Computational efficiency is achieved by decomposing the six-dimensional circle estimation problem into two sequential three-dimensional estimation problems. Simulation and experiment are used to evaluate the proposed method.

In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously detect and track the road farther away from the ego vehicle. In addition the particles are used to generate static road segment estimations in a given look ahead distance. These estimations are predicted with respect to ego motion and fused utilizing Kalman filter techniques to generate a smooth local clothoid segment for lateral control of the vehicle.