Ground Truth Generation for Quantitative Performance Evaluation of Localization Methods in Urban Areas

Takeda, Yuji; Tsuchiya, Chikao; Khiat, Ali

doi:10.1109/ivs.2019.8813891

Cited by 3 publications

(1 citation statement)

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“…For the validation of these models, one requires ground truth data [1], whose quality obviously has to improve with the increased quality demands of environment models. It is foreseeable that classical methods for obtaining ground truth data are quickly reaching their limits, e.g., differential global positioning systems (DGPS) for localization in urban areas [2]. Thus, this paper investigates a new method: By tracking vehicles for which a precise 3D model is given, we can obtain ground truth data of unmatched accuracy.…”

Section: Introductionmentioning

confidence: 99%

Model-based Offline Vehicle Tracking in Automotive Applications Using a Precise 3D Model

Althoff

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Object tracking aims at estimating the state of moving objects based on remote measurements. To evaluate online algorithms in automotive systems, ground truth data must be acquired, which is a time-consuming and expensive approach. We propose a novel offline approach to generate ground truth data from existing sensor measurements using CAD models. In our approach, we provide error bounds for the localization of the objects based on the measurement noise of a single laser beam and the sensitivity of the point cloud registration. To estimate accurate kinematic states of the vehicle, we apply an extended Rauch-Tung-Striebel smoother on the stored measurements. In experiments with real sensor data, we demonstrate that the performance of the proposed approach is superior to DGPS within the near range.

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Section: Introductionmentioning

confidence: 99%