It became a well known technology that a map of complex environment containing low-level geometric primitives (such as laser points) can be generated using a robot with laser scanners. This research is motivated by the need of obtaining semantic knowledge of a large urban outdoor environment after the robot explores and generates a low-level sensing data set. An algorithm is developed with the data represented in a range image, while each pixel can be converted into a 3D coordinate. Using an existing segmentation method that models only geometric homogeneities, the data of a single object of complex geometry, such as people, cars, trees etc., is partitioned into different segments. Such a segmentation result will greatly restrict the capability of object recognition. This research proposes a framework of simultaneous segmentation and classification of range image, where the classification of each segment is conducted based on its geometric properties, and homogeneity of each segment is evaluated conditioned on each object class. Experiments are presented using the data of a large dynamic urban outdoor environment, and performance of the algorithm is evaluated.

This work is motivated by the following two potential applications: 1) enhancing driving safety and 2) collecting traffic data in a large dynamic urban environment. A laser-scanner-based approach is proposed. The problem is formulated as a simultaneous localization and mapping (SLAM) with object tracking and classification, where the focus is on managing a mixture of data from both dynamic and static objects in a highly dynamic environment. A trajectory-oriented closure is also proposed using the sporadically available global positioning system (GPS) measurements in urban areas to assist for global accuracy, particularly when the vehicle makes a noncyclical measurement in a large outdoor environment. Experiments are conducted using the data that were collected along a course near 4. 5 km in a highly dynamic environment. Possibilities of the approaches toward the two potential applications are demonstrated, and avenues for future works are discussed.

Motivated by two potential applications, i. e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. This research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. In this research, a number of features are defined after examining the property of data appearance. A classification method is proposed after examining the likelihood measures between each pair of feature and class. Experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.

In this research, a novel system for mon- itoring an intersection using a network of single-row laser range scanners (subsequently abbreviated as "laser scanner") is proposed. Laser scanners are set on the road side to profile an intersection horizontally from different viewpoints. This is done so that cross sections of the intersection are captured at a high scanning rate (e. g., 37Hz) and to contain the contour points of the moving objects entering the intersection. Different laser scanners data are integrated into a common spatial-temporal coordinate system and processed. Thus, the moving objects inside the intersection are detected and tracked to estimate their state parameters, such as: location, speed, and direction at each time instance. An experiment was conducted in central Beijing, where six laser scanners were used to cover a three-way intersection. A digital copy of the dynamic intersection was measured, and, through data processing, a large quantity of physical dimension traffic data was obtained.

This research is motivated by two potential applications-enhancing driving safety and collecting traffic data in a large dynamic urban environment. A laser scanner based approach is proposed, in which SLAM (simultaneous localization and mapping) is developed with moving object detection and tracking using a laser scanner for perception, using GPS to achieve global accuracy, and using yaw rate and wheel speed to diagnose pose errors. Experiments are conducted to collect data along a course (4. 5 km) with a test-bed vehicle run in a highly dynamic environment. The algorithms are examined, possibilities with respect to the two potential applications are demonstrated, and future works are discussed.

In this research, we propose a method of SLAM in a dynamic large outdoor environment using a laser scanner. Focus are cast on solving two major problems: 1) achieving global accuracy especially in non-cyclical environment, 2) tackling a mixture of data from both dynamic and static objects. Algorithms are developed, where GPS data and control inputs are used to diagnose pose error and guide to achieve a global accuracy; Classification of laser points and objects are conducted not in an independent module but across the processing in a framework of SLAM with moving object detection and tracking. Experiments are conducted using the data from two test-bed vehicles, and performance of the algorithms are demonstrated.

In this research, we proposed a system of detecting and monitoring pedestrians' motion trajectories at a populated and wide environment, such as exhibition hall, supermarket etc., using the horizontally profiling single-row laser range scanners on a mobile platform. A simplified walking model is defined to track the rhythmic swing feet at the ground level. Pedestrians are recognized by detecting the braided styles, which is a typical appearance that could discriminate the data of moving feet with other mobile and motionless objects. Two experiments are conducted. One is at the laboratory environment, the purpose of which is to examine the algorithm in details. Another is at an exhibition hall, a populated and wide environment, the purpose is to examine whether the system could be applied for practical needs. It is a big challenge, while the system did well. Pedestrians in the exhibition hall at the moment of measurement are detected. Their motion trajectories are extracted, and associated to the background map, which is made of the motionless objects, and covers the whole exhibition hall.

The Vehicle-borne Laser Measurement System (VLMS) is a mobile mapping system where three single-row laser range scanners, six line CCD cameras, and a GPS/INS-based navigation unit are mounted on a van to measure object geometry and texture along a street. This paper contributes to a method of fusing the data output of VLMS with existing geographic data sources where the focus is on the rectification of the vehicle's motion data derived from GPS/INS, which may be quite erroneous in an urban area. An algorithm is developed to correct the four variables of the vehicle's motion data, i. e., x, y, and z coordinates of the vehicle position and yaw angle of orientation by registering the laser points of the VLMS with an existing data source, e. g., a DSM. The algorithm was examined using the VLMS data that were taken in the Ginza District, Tokyo. By manually assigning 18 sets of tiepoints, the vehicle's motion data were corrected automatically and efficiently, such that the laser points of VLMS were matched to a DSM. In data fusion, a set of objects, which contained commercial signboards, traffic signs and signals, road boundaries, and road lights were extracted from the corrected VLMS data using an interface described in a previous paper. They were integrated with a 1:2500 3D map that consists of only building frames. In addition, line images from the VLMS were projected onto the building facades of the 3D map, and textures were generated automatically.

In this research, we propose a novel system for tracking pedestrians in a wide and open area, such as a shopping mall and exhibition hall, using a number of single-row laser-range scanners (LD-A), which have a profiling rate of 10 Hz and a scanning angle of 270. LD-As are set directly on the floor doing horizontal scanning at an elevation of about 20 cm above the ground, so that horizontal cross sections of the surroundings, containing moving feet of pedestrians as well as still objects, are obtained in a rectangular coordinate system of real dimension. The data of moving feet are extracted through background subtraction by the client computers that control each LD-A, and sent to a server computer, where they are spatially and temporally integrated into a global coordinate system. A simplified pedestrian’s walking model based on the typical appearance of moving feet is defined and a tracking method utilizing Kalman filter is developed to track pedestrian’s trajectories. The system is evaluated through both real experiment and computer simulation. A real experiment is conducted in an exhibition hall, where three LD-As are used covering an area of about 60 60 m2. Changes in visitors’ flow during the whole exhibition day are analyzed, where in the peak hour, about 100 trajectories are extracted simultaneously. On the other hand, a computer simulation is conducted to quantitatively examine system performance with respect to different crowd density.

In this research, a novel vehicle-borne system of measuring three–dimensional (3-D) urban data using single-row laser range scanners is proposed. Two single-row laser range scanners are mounted on the roof of a vehicle, doing horizontal and vertical profiling respectively. As the vehicle moves ahead, a horizontal and a vertical range profile of the surroundings are captured at each odometer trigger. The freedom of vehicle motion is reduced from six to three by assuming that the ground surface is flat and smooth so resulting in the vehicle moving on almost the same horizontal plane. Horizontal range profiles, which have an overwhelming overlay between successive ones, are registered to trace vehicle location and attitude. Vertical range profiles are aligned to the coordinate system of the horizontal one according to the physical geometry between the pair of laser range scanners, and subsequently to a global coordinate system to make up 3-D data. An experiment is conducted where 3-D data of a real urban scene is obtained by registering and integrating 2412 horizontal and vertical range profiles. Two ground truths are used in examination. They are the outputs of a GPS/INS/Odometer based positioning system and a 1:500 digital map of the testing site. Accuracy and efficiency of the method in measuring 3-D urban scene is demonstrated.