Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Costa, CM (Author) Other; Sobreira, HM (Author) Other; Armando Jorge Sousa (Author) FEUP; Germano Veiga (Author) FEUP

Journal

Title: Robotics and Autonomous Systems

Vol. 76

Pages: 113-140

ISSN: 0921-8890

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00J-ZF0

DOI: 10.1016/j.robot.2015.09.030

Abstract (EN): Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 28