Vision-based Approach to Obstacle Avoidance

This paper describes a 3D obstacle modeling system which uses a 2D vision sensor. Prior work tracks feature points in a sequence of images and estimates their positions. However, in this paper we use obstacle edges instead. Using an image segmentation technique, edges are detected as line segments. Subsequently, these edges are modeled in a 3D space from the measured line segments using known camera motions. The z-test method is used for corresponding estimated line data with measurements. Line addition and deletion algorithms are also explained. Simulation results show that simple structures are accurately modeled by the suggested line-based estimator. Finally, this method is applied to a 3D terrain mapping problem. I. Introduction Unmanned aerial vehicles (UAVs) play an important role in military operations and have significant potential for commercial applications. UAVs are expected to operate in dangerous areas, such disaster site or enemy territory, and they can provide realtime information to the user. Various problems in UAV automation are still under investigation. One of these is obstacle detection and avoidance. If a vehicle operates in close proximity to unknown terrain or structures, its navigation system has to automatically detect obstacles and its guidance and control systems must avoid collisions with them. For obstacle detection, it is ideal to obtain 3D site mapping data of the terrain over which the UAV flies. Laser rangefinders can provide very accurate environmental data, 1 however, they are too large and heavy to install on small UAVs. Moreover, they are very expensive. Thus, a single 2D camera is chosen as a sensor for obstacle detection. It is reasonable to use a camera because they are low cost and meet the size and weight constraints of most small UAVs. Furthermore, a camera can be used to obtain sufficient information of the vehicles unknown operational environment in realtime. This paper considers the design of a 3D site modeling system using a single 2D camera. In some studies, vision-based terrain modeling is achieved by tracking many feature points in a sequence of images and updating estimates of their actual 3D positions 2 . 3 Unlike these studies, this paper describes the estimator design based on line information, instead of points. In general, edges of obstacles may appear as curved lines of finite length in an image. In particular, most artificial structures such as buildings have straight edges which appear as a set of straight line segments in an image. Therefore, our objective is to estimate actual obstacle edge lines from the line segments which are detected in an image through an image segmentation technique, and to create a 3D model of the obstacles. It is notable that the line-based estimator uses more structural information (points and their connectivity) than the point-based estimator to create an obstacle model. First, every line segment in a given measurement set is matched with estimated line data. The statistical z-test value is introduced to perform this correspondence. 4 The z-test value is taken for a certain error index. Then the z-test value is inversely related to the likelihood of an event that a given measurement corresponds to the line data chosen. When using the z-test, both estimation error and measurement error covariances are taken in account. For each measurement, the z-test value is calculated and a line which attains the least value is chosen. After a line is assigned, an extended Kalman filter (EKF) is applied to update the two endpoint positions for each line from the residuals of the two endpoints of the measured line segment