A Study on Obstacle Avoidance and Person Following for Autonomous Land Vehicle Guidance in Indoor Environments Using Computer Vision and Pattern Recognition Techniques

• Main Authors: Ching-Heng Ku, 顧靜恆
• Other Authors: Wen-Hsiang Tsai
• Format: Others
• Language: en_US
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/34286267720588775262

博士 === 國立交通大學 === 資訊科學系 === 88 === Autonomous land vehicles (ALV''s) are very useful in many automation applications because the ability of autonomous navigation can save a lot of manpower. Furthermore, the navigation method by which a person can lead the ALV to any desired place creates more applications of the ALV system. The system can be used as an autonomous handcart, go-cart, buffet car, shopping car, dust cart, golf cart, weeder, etc. in various applications. Besides, the system can also be used as a learning system to collect information in certain environments, including open paths or locations of obstacles. In order to achieve successful navigation, the ALV requires integration of techniques of environment sensing and learning, image processing and feature extraction, ALV location estimation and path planning, speed and wheel control, and so on. Besides, the ALV has to make decisions automatically for safe, smooth, and robust navigation. For these purposes, four approaches to obstacle avoidance and person following for vision-based autonomous land vehicle guidance in indoor environments using computer vision and pattern recognition techniques are proposed in this dissertation. In the first approach, a vision-based approach to obstacle avoidance for ALV navigation in indoor environments is proposed. The approach is based on the use of a pattern recognition scheme, the quadratic classifier, to find collision-free paths in unknown indoor corridor environments. Obstacles treated in this approach include walls and objects that appear in the way of ALV navigation in the corridor. Detected obstacles as well as the two sides of the ALV body are considered as patterns, which, after categorized into two classes, are used as input to a quadratic classifier. Finally, the two-dimensional decision boundary of the classifier, which is enforced to go through the middle point between the two front vehicle wheels, is taken as a local collision-free path. In the second approach, a new approach to ALV navigation by person following is proposed. This approach is based on sequential pattern recognition and computer vision techniques, and maintenance of smoothness for indoor navigation is the main goal. Sequential pattern recognition is used to design a classifier for making decisions about whether the person in front of the vehicle is walking straight, or is too right or too left to the vehicle. Multiple images in a sequence are used as input to the system. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. In the third approach, a robust trajectory planning method for vision-based ALV guidance by person following using a visual field model is proposed. The visual field model contains a visible area and a person-bounded area. When the ALV navigates by following a walking person in front, the person has to be detected in each cycle from the image captured by a camera. The proposed trajectory planning method aims to guide the ALV to make the followed person always appear in the image. It is found in this study that if three visual contact constraints in the visual field model are satisfied, this goal can be achieved. The first constraint postulates that the person has appeared in the image. The second constraint requires that the direction of the vehicle head at the next position point straightly forward to the person''s current position. The third constraint expects that the distance between the next position of the ALV and the current position of the people be bounded in a certain range. A formula for the trajectory of the vehicle that satisfies the second visual contact constraint is derived. Furthermore, two theorems specifying some conditions for the derived trajectory to be applicable to practical navigation are also derived. The steps for generating a speed and a turn angle for the ALV to conduct real-time navigation are described as a trajectory planning algorithm. Finally, the approach was tested on a real ALV. In the fourth approach, an obstacle avoidance method for use in person following for vision-based ALV guidance is proposed. This method is based on the use of vehicle location estimation and a quadratic pattern classifier, and aims to guide the ALV to follow a walking person in front by navigating along a derived collision-free path. Before generating the collision-free path, the person''s location is obtained from extracted objects in the image by a person detection method. The object closest to a predicted person location is regarded as the followed person and the remaining objects are regarded as obstacles. The collision-free navigation path is designed for ALV guidance in such a way that the ALV not only can keep following the person but also can avoid collision with nearby obstacles. The navigation path results from a quadratic classifier that uses the vehicle and all of the objects, including the person, in the image as input patterns. A turn angle is then computed to drive the ALV to follow the navigation path. Our approaches are all implemented on a real ALV, and successful, safe, smooth, and robust navigation sessions confirm the feasibility of the approaches.