Appearance-based vehicle localization across seasons in a metric map

Vehicle localization;Robotics;Computer vision;SLAM

Beall, Christopher Allan ; Dellaert, Frank Electrical and Computer Engineering Yezzi, Anthony Rehg, James Vela, Patricio Christensen, Henrik Sibley, Gabe ; Dellaert, Frank

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Vehicle localization;    Robotics;    Computer vision;    SLAM;   

Others  :  https://smartech.gatech.edu/bitstream/1853/55684/1/BEALL-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Great strides have been made in recent years in developing the necessary technologies to make autonomous cars a reality. However, a number of challenges remain, a major one being that of accurate vehicle localization. This thesis presents a vision-only approach to the outdoor localization problem. The system provides for real-time, metric localization of a moving camera (on a vehicle) in a pre-built 3D map, which is inherently robust with respect to appearance changes. This is achieved by utilizing a novel spatio-temporal map (STM) representation which is built up from multiple drives worth of stereo camera data, as well as a localization algorithm which efficiently retrieves landmarks from the STM to perform appearance-based localization in real-time. The STM encodes the landmark visibility structure of the datasets which were captured to build the map, as well as landmark descriptors and observation times. This visibility structure and meta-data are then exploited for efficient localization. In addition, by splitting the STM up into a number of submaps, computational tractability is ensured during the map-building phase, as well as during localization. Experiments on real data validate that the presented method works better than conventional approaches which operate in a map built of a single dataset.