Showing papers by "Larry Matthies published in 1987"

Error modeling in stereo navigation

Abstract: In stereo navigation, a mobile robot estimates its position by tracking landmarks with on-board cameras. Previous systems for stereo navigation have suffered from poor accuracy, in part because they relied on scalar models of measurement error in triangulation. Using three-dimensional (3D) Gaussian distributions to model triangulation error is shown to lead to much better performance. How to compute the error model from image correspondences, estimate robot motion between frames, and update the global positions of the robot and the landmarks over time are discussed. Simulations show that, compared to scalar error models, the 3D Gaussian reduces the variance in robot position estimates and better distinguishes rotational from translational motion. A short indoor run with real images supported these conclusions and computed the final robot position to within two percent of distance and one degree of orientation. These results illustrate the importance of error modeling in stereo vision for this and other applications.

Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representataion

Abstract: Multiple range sensors are essential in mobile robot navigation systems. This introduces the problem of integrating noisy range data from multiple sensors and multiple robot positions into a common description of the environment. We propose a cellular representation called the occupancy grid as a solution to this problem. In this paper, we use occupancy grids to combine range information from sonar and one-dimensional stereo into a two-dimensional map of the vicinity of a robot. Each cell in the map contains a probabilistic estimate of whether it is empty or occupied by an object in the environment. These estimates are obtained from sensor models that describe the uncertainty in the range data. A Bayesian estimation scheme is used to update the existing map with successive range profiles from each sensor. This representation is simple to manipulate, treats different sensors uniformly, and models uncertainty in the sensor data and in the robot position. It also provides a basis for motion planning and creation of higherlevel object descriptions.