Masaki Chiba

SLAM in a dynamic large outdoor environment using a laser scanner

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.

A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data Collection

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.

Moving object classification using horizontal laser scan data

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.

Driving safety and traffic data collection - A laser scanner based approach

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.