Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
This work addresses the challenge of baseline generation for radar-based tracking in automotive applications, specifically for pedestrians and cyclists, though it is incremental as it builds on existing sensor fusion approaches.
The paper tackles the problem of generating accurate ground truth trajectories for automotive radar tracking by using a portable GNSS and IMU system to automatically acquire reference data for vulnerable road users (VRUs). It results in more precise real-world radar data distributions compared to conventional methods, with evaluations under different GNSS conditions.
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate hand-held global navigation satellite system (GNSS). An embedded inertial measurement unit (IMU) is used for estimating orientation and motion behavior. This article contains two major contributions. A method for associating radar data to vulnerable road user (VRU) tracks is described. It is evaluated how accurate the system performs under different GNSS reception conditions and how carrying a reference system alters radar measurements. Second, the system is used to track pedestrians and cyclists over many measurement cycles in order to generate object centered occupancy grid maps. The reference system allows to much more precisely generate real world radar data distributions of VRUs than compared to conventional methods. Hereby, an important step towards radar-based VRU tracking is accomplished.