Aerial Image Based Heading Correction for Large Scale SLAM in an Urban Canyon

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 229
  • Download : 0
This letter presents an effective solution for 3D urban map generation using aerial images as prior information for Simultaneous Localization and Mapping (SLAM). The research targets urban canyons where Global Positioning System (GPS) signals are highly sporadic and erroneous. As a solution for urban canyon mapping, this study proposes aerial image-based heading correction and kinematics considered odometry modeling. Aerial images often suffer from being significantly askew due to the height of structures and viewpoint changes, preventing direct integration with SLAM. However, we found that the direction of the structural edges was still valid, despite slanted building images. Making use of this property, this letter proposes a heading correction method using aerial images. As heading error is a critical factor in SLAM when mapping large areas, the proposed heading correction method substantially reduces estimation errors. Aiming for large urban area mapping, this letter also focuses on sensor modeling for odometry and GPS data. In odometry generation, mobile robot kinematics is considered by incorporating wheel diameter and base distance uncertainties into odometry covariance modeling. Results are presented from various types of urban areas over a path 28 km in length. For thorough validation, we ran the algorithm on three urban areas with differing degrees of GPS availability and structural complexity: a downtown area with medium complexity, a building complex, and the downtown area of a large city.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-10
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.2, no.4, pp.2232 - 2239

ISSN
2377-3766
DOI
10.1109/LRA.2017.2725439
URI
http://hdl.handle.net/10203/227067
Appears in Collection
CE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0