Showing papers by "Wen-Hsiang Tsai published in 1999"

Viewing corridors as right parallelepipeds for vision-based vehicle localization

Abstract: A approach to vision-based vehicle localization by viewing corridors as a combination of right parallelepipeds is proposed. The objective is to derive the orientation and lateral position of a vehicle in a right parallelepiped corridor. These two kinds of information are all that is needed for vehicles to navigate safely in a right parallelepiped corridor. This approach offers low hardware cost and simple computation. Only one camera mounted on the vehicle is needed, and analytical formulae are derived for computing the vehicle location. The information source is the corridor ceiling. Two orthogonal sets of parallel lines on the corridor ceiling are used to detect the vanishing line of the ceiling. An equation is developed to derive the vehicle orientation by utilizing the detected vanishing line. Also, based on the observation of the variation of image line slopes when the vehicle moves laterally, another equation is established to evaluate the relative lateral position of the vehicle by utilizing the line slope of the ceiling line pointing forward. Experiments have been conducted, and acceptable vehicle localization results have been obtained, to prove the feasibility of the proposed approach.

Reliable determination of object pose from line features by hypothesis testing

Abstract: To develop a reliable computer vision system, the employed algorithm must guarantee good output quality. In this study, to ensure the quality of the pose estimated from line features, two simple test functions based on statistical hypothesis testing are defined. First, an error function based on the relation between the line features and some quality thresholds is defined. By using the first test function defined by a lower bound of the error function, poor input can be detected before estimating the pose. After pose estimation, the second test function can be used to decide if the estimated result is sufficiently accurate. Experimental results show that the first test function can detect input with low qualities or erroneous line correspondences and that the overall proposed method yields reliable estimated results.

Estimation of moving vehicle locations using wheel shape information in single 2-D lateral vehicle images by 3-D computer vision techniques☆

Abstract: An approach to the estimation of moving lateral vehicle locations for driving assistance using wheel shape information in single 2-D vehicle images by 3-D computer vision techniques is proposed. The location scheme is supposed to be performed on a vehicle with a camera mounted on the front bumper. An analytical solution is applied to estimate locations of the lateral vehicle. Firstly, the rear wheel shape of a lateral vehicle moving in a nearby lane is imaged. By using the Hough transform, the projected wheel shape, which is an ellipse, is detected. Secondly, the equation of the detected ellipse is used to infer the orientation angle of the lateral vehicle with respect to the camera view direction. Finally, the center of the ellipse shape is used to determine the relative position of the lateral vehicle with respect to the camera lens center. Moreover, an edge-point veri"cation algorithm is utilized to extract the ellipse shape more precisely in the image processing stage. Both computer simulated and real images are tested and good experimental results show the e!ectiveness of the proposed approach for estimating lateral vehicle locations. The results are useful for driving assistance and vehicle collision avoidance and are discussed in detail. ( 1999 Elsevier Science Ltd. All rights reserved.

Obstacle avoidance for autonomous land vehicle navigation in indoor environments by quadratic classifier

Abstract: A vision-based approach to obstacle avoidance for autonomous land vehicle (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 study include the walls of the corridor and the 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. A systematic method for separating these patterns into two classes is proposed. The two pattern classes are used as the input data to design a quadratic classifier. Finally, the two-dimensional decision boundary of the classifier, which goes through the middle point between the two front vehicle wheels, is taken as a local collision-free path. This approach is implemented on a real ALV and successful navigations confirm the feasibility of the approach.

Smooth vision‐based autonomous land vehicle navigation in indoor environments by person following using sequential pattern recognition

Abstract: A new approach to autonomous land vehicle ALV navigation by the person follow- ing is proposed. This approach is based on sequential pattern recognition and com- puter vision techniques, and maintenance of smoothness for indoor navigation is the main goal. The ALV is guided automatically to follow a person who walks in front of the vehicle. The vehicle can be used as an autonomous handcart, go-cart, buffet car, golf cart, weeder, etc. in various applications. 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 of the vehicle. Multiple images in a sequence are used as input to the system. Computer vision techniques are used to detect and locate the person in front of the vehicle. By sequential pattern recognition, the relation between the location of the person and that of the vehicle is categorized into three classes. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. The approach is implemented on a real ALV, and successful and smooth navigation sessions confirm the feasibility of the approach.

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation ☆

Abstract: A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.