Kälin, U.; Staffa, L.; Grimm, D.E.; Wendt, A. Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios. Remote Sens.2022, 14, 90.
Kälin, U.; Staffa, L.; Grimm, D.E.; Wendt, A. Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios. Remote Sens. 2022, 14, 90.
Kälin, U.; Staffa, L.; Grimm, D.E.; Wendt, A. Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios. Remote Sens.2022, 14, 90.
Kälin, U.; Staffa, L.; Grimm, D.E.; Wendt, A. Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios. Remote Sens. 2022, 14, 90.
Abstract
To validate the accuracy and reliability of onboard sensors for object detection and localization in driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle (e.g. car during parking maneuvers), independent of the onboard sensors, during test maneuvers within a reference environment. One requirement is a 6 degree of freedom (DoF) pose with a position uncertainty below 5 mm (3σ), an orientation uncertainty below 0.3° (3σ) at a frequency higher than 20 Hz, and a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, a synchronization via Precision Time Protocol (PTP) and a system interoperability to Robot Operating System (ROS) is implemented. The developed system combines motion capture cameras mounted in a 360° panorama view set-up on the vehicle with robotic total stations. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is simulated. Results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met, while allowing a flexible installation in different environments.
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