Sensor Data Integrity : Multi-Sensor Perception for Unmanned Ground Vehicles

This document describes large, accurately calibrated and time-synchronised datasets, gathered in controlled environmental conditions, using an unmanned ground vehicle equipped with a wide variety of sensors. These sensors include: multiple laser scanners, a millimetre wave radar scanner, a colour camera and an infra-red camera. Full details of the sensors are given, as well as the calibration parameters needed to locate them with respect to each other and to the platform. This report also specifies the format and content of the data, and the conditions in which the data have been gathered. The data collection was made in two different situations of the vehicle: static and dynamic. The static tests consisted of sensing a fixed ’reference’ terrain, containing simple known objects, from a motionless vehicle. For the dynamic tests, data were acquired from a moving vehicle in various environments, mainly rural, including an open area, a semi-urban zone and a natural area with different types of vegetation. For both categories, data have been gathered in controlled environmental conditions, which included the presence of dust, smoke and rain. Most of the environments involved were static, except for a few specific datasets which involve the presence of a walking pedestrian. Finally, this document presents illustrations of the effects of adverse environmental conditions on sensor data, as a first step towards reliability and integrity in autonomous perceptual systems.

[1]  Steve Scheding,et al.  Calibration of range sensor pose on mobile platforms , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Sauro Longhi,et al.  Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles: Experimental Validation (part II) , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[3]  Simon Lacroix,et al.  Selection and Monitoring of Navigation Modes for an Autonomous Rover , 2006, ISER.

[4]  Robert Pless,et al.  Extrinsic calibration of a camera and laser range finder (improves camera calibration) , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[5]  Gamini Dissanayake,et al.  Mutual Information based Sensor Registration and Calibration , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Sauro Longhi,et al.  Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles: Theoretical Aspects (part I) , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.