Novel Vehicle Bounding Box Tracking Using a Low-End 3D Laser Scanner

An, Jhonghyun; Kim, Euntai

doi:10.1109/tits.2020.2994624

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…After correcting the bounding box's size according to the preset vehicle size, the pose estimation of the vehicle was finally completed. This method was more stable and easier to calculate compared to that of Kim et al [18].…”

Section: Methods Based On Global Algorithmsmentioning

confidence: 89%

“…Although the algorithm fitted the vehicle measurement model well, the fitting randomness caused by the scaling series algorithm interfered with the pose estimation. An and Kim [18] proposed an algorithm for pose estimation using a low-end 3D LiDAR. The algorithm first established four obstacle vehicle models with different observation angles, then modeled the measured size of the object vehicle as a uniformly distributed sample, and finally used the idea of template matching to estimate the pose of the object vehicle.…”

Section: Methods Based On Global Algorithmsmentioning

confidence: 99%

“…Although pose estimation is only a small part of the traditional 3D point cloud processing process, it is significant for the accuracy of object tracking algorithms because it is used as the input of the tracking algorithm [18]. The fluctuation error of pose estimation can cause the tracking algorithm to be unable to effectively track an obstacle's trajectory.…”

Section: Challenge Of Pose Estimationmentioning

confidence: 99%

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Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

et al. 2021

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Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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Section: Methods Based On Global Algorithmsmentioning

confidence: 89%

Section: Methods Based On Global Algorithmsmentioning

confidence: 99%

Section: Challenge Of Pose Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

et al. 2021

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show abstract

“…In model-bases direct tracking DATMO algorithms, prior knowledge about the geometric shape and dynamic model of the moving vehicles are used to track the states and the geometric shape of the objects [15], [16] without detecting the objects first [9], [18]. Tracking the geometry helps to predict the dynamic properties with higher precision and discard tracked objects with strange shapes or geometry changes.…”

Section: B Direct Trackingmentioning

confidence: 99%