Urban@CRAS dataset: Benchmarking of visual odometry and SLAM techniques

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Ana Rita Gaspar (Author) Other; Alexandra Nunes (Author) Other; Andry Maykol Pinto (Author) FEUP; Aníbal Matos (Author) FEUP

Journal

Title: Robotics and Autonomous Systems

Vol. 109

Pages: 59-67

ISSN: 0921-8890

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 17 Citations

INSPEC

Other information

Authenticus ID: P-00P-GQA

DOI: 10.1016/j.robot.2018.08.004

Abstract (EN): Public datasets are becoming extremely important for the scientific and industrial community to accelerate the development of new approaches and to guarantee identical testing conditions for comparing methods proposed by different researchers. This research presents the Urban@CRAS dataset that captures several scenarios of one iconic region at Porto Portugal These scenario presents a multiplicity of conditions and urban situations including, vehicle-to-vehicle and vehicle-to-human interactions, cross-sides, turn-around, roundabouts and different traffic conditions. Data from these scenarios are timestamped, calibrated and acquired at 10 to 200 Hz by through a set of heterogeneous sensors installed in a roof of a car. These sensors include a 3D LIDAR, high-resolution color cameras, a high-precision IMU and a GPS navigation system. In addition, positioning information obtained from a real-time kinematic satellite navigation system (with 0.05m of error) is also included as ground-truth. Moreover, a benchmarking process for some typical methods for visual odometry and SLAM is also included in this research, where qualitative and quantitative performance indicators are used to discuss the advantages and particularities of each implementation. Thus, this research fosters new advances on the perception and navigation approaches of autonomous robots (and driving).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9