License Plate Recognition (LPR) System is one kind of Intelligent Transportation Systems (ITS) and is of considerable interest because of its potential applications to such areas as highway electronic toll collection, red-light violation enforcement, secure-access control at parking lots and so on. This paper introduces the system frame and work flow of our LPR System which works based on the web technique. Some problems of the technical emphases and realization are also addressed here. Capabilities under practical conditions show the effectiveness at the end of this paper.

In this paper, a new method of initial localization for a mobile robot only employing a laser range finder is proposed. We accomplish this by the matching of "Complete Line Segments (CLS)" derived from current scan of range finder and the priori-map. The definition of "Complete Line Segments (CLS)" and "Sub-Complete Line Segments (SLS)" as well as their properties and decision rules are given. We confirm that, in the angle of theory, the initial localization can be accompli shed even if only one pair of CLS can be matched exactly since the CLS represent some basic but complete features of the environment. So compare with other ways, this method has simpler principle and can easily be implemented with less computational costs. The whole process of initial localization for a mobile robot is described in detail. At last promising real-world experiments involving the initial localization modules on our ATRV2 mobile robot are described.

Position estimation based on scan matching is a type of popular approaches for mobile robat localization in indoor office-like environments. Globai scan matching and local scan mat ching are two basic position estimation problems. Global scan matching is subject to global localization for mobile robat and local scon matching is used to deal with tracking mobile robot position over time. Global localizution based on scon maiching is represented in this paper. A global map is built to represent whole environment feature and a local map is built to represent current range scan, Global map and local map consist of a lot of tine segments. Compltte line segment that represents a complete environment object is singled out and used to match between local map and giobal map. Line segments are sorted counterclockwise in two maps.This type of ardinal map improves the searching efficiency of matching and loculization algorithm is limesaving. In addition, matching between current range scan and globul map is hased on relative position relationships of line segments in local and glohal map. Glohal localization based on relative psition relationship Avoids frequent coordinates transformation and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.

A novel preprocessing and localization method has been proposed for container auto recognition system. The method combines both linerar filters and nonlinera filters to reducs noises on an image. Selection method of the adaptive parameter of the filters in discussde thoroughly. Tests have been made based on the mehod, Character Lines can be properly located with a ratio of above 98%.

It is very necessary for Autonomous Land Vehicle (ALV) to hove a reliable abiliiy of obstacle detection. Unlike srructural highway, environmems, where only vehicles ahead of the ALV are needed to consider, small obstacles are more ofien seen in semi-structural environments. In this paper we described a small obstacle detection qsrem for ALV under such ein.iroiiinenrs. The system consists of hvo 2D Laser Range Finders (LRE) which are responsible for obstacle recognizing and tracking respectively. For iop LRF. a D-S evidence riieon, based ousiacle recognition algorithm is used to distringuish the obstacles from rhe brushwood on the roadsides. Based on that. an obstacle tracking process ir sraricd bj, uillsing the data from borrom LRF. Besides the obstacles, the distribution of the brushwood on roadsides, which could be used,ior path planning. is also obtained. The experimenral results demonstrate our success.