2019 20th International Radar Symposium (IRS) 2019
DOI: 10.23919/irs.2019.8768169
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Automated Ground Truth Estimation for Automotive Radar Tracking Applications With Portable GNSS And IMU Devices

Abstract: 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 articl… Show more

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Cited by 12 publications
(4 citation statements)
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“…Our dataset is based on the data recorded for [1]. The original dataset has annotations created by an automatic labeling system [32]. For this article, we provide manual and detailed annotations for all types of multi-path reflections.…”
Section: Datasetmentioning
confidence: 99%
“…Our dataset is based on the data recorded for [1]. The original dataset has annotations created by an automatic labeling system [32]. For this article, we provide manual and detailed annotations for all types of multi-path reflections.…”
Section: Datasetmentioning
confidence: 99%
“…For example, CARRADA and CRUW use a semi-automatic annotation approach transforming camera label predictions to the radar domain. In the NLOS-Radar data set, instructed test subjects carry a GNSS-based reference system from [7] for automatic label generation. Moreover, lidar data is a common source for generating automated labels.…”
Section: B Labelingmentioning
confidence: 99%
“…In the second part the resolutions ∆ r , ∆ φ , ∆ vr and ∆ t for r, φ, v r , and time t are noted. The radar data is labeled using a global navigation satellite system (GNSS) reference which is mounted in a wearable backpack following [14]. All automatically labeled data were manually checked and corrected if necessary.…”
Section: Multi-path Detectionsmentioning
confidence: 99%