An Orientation Corrected Bounding Box Fit Based on the Convex Hull under Real Time Constraints

Naujoks, Benjamin; Wuensche, Hans-Joachim

doi:10.1109/ivs.2018.8500692

Cited by 22 publications

(10 citation statements)

References 20 publications

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“…However, when the point clouds were sparse, the particles' randomness became strong, which reduced the accuracy of pose estimation. In response to this problem, Naujioks and Wuensche [35] proposed an algorithm that used a convex hull to make a preliminary estimate and a line-creation heuristic to fit a bounding box around incomplete segments of point clouds to correct vehicle orientation. This algorithm used the rapidity features of convex hull extraction, which could efficiently perform pose estimation even when the point cloud was sparse.…”

Section: Methods Based On Global Algorithmsmentioning

confidence: 99%

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: 99%

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

et al. 2021

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“…Jaspers et al also include information from the tracking system of Himmelsbach and Wünsche [15] to exclude moving parts from the mapping process. Recent publications focus on enhancing clustering and hypotheses generation algorithms [18], [19]. As one of few groups which regularly participate in land robot trials, their work considers real-time constraints and has been shown to work in closed-loop driving.…”

Section: Overview Of Similar Workmentioning

confidence: 99%

A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

Rieken,

Maurer

2020

Preprint

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We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of nonflat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion classification stages.Based on the pipeline's output, the stationary environment is represented in a multi-feature mapping and fusion approach. Movable elements are represented in an object tracking system capable of using multiple reference points to account for viewpoint changes. We further enhance the tracking system by explicit consideration of occlusion and ambiguity cases.Our system is evaluated using a subset of the TUBS Road User Dataset. We enhance common performance metrics by considering application-driven aspects of real-world traffic scenarios. The perception system shows impressive results and is able to cope with the addressed scenarios while still preserving real-time capability.

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“…During the scenario, different occlusions (only the back or one side can be seen) of the segmented vehicle and segmentation errors, e. g., falsely associated ground plane points, occur. Moreover, the bounding boxes of the PT approach are obtained with [29]. The chosen number of points in the scenarios are a compromise between better estimation results and faster run time.…”

Section: B Nurbs Shape Functionmentioning

confidence: 99%

Fast 3D Extended Target Tracking using NURBS Surfaces

Naujoks

Burger

Wuensche

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

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This paper proposes fast and novel methods to jointly estimate the target's unknown 3D shape and dynamics. Measurements are noisy and sparsely distributed 3D points from a light detection and ranging (LiDAR) sensor. The methods utilize non-uniform rational B-splines (NURBS) surfaces to approximate the target's shape. One method estimates Cartesian scaling parameters of a NURBS surface, whereas the second method estimates the corresponding NURBS weights, too. Major advantages are the capability of estimating a fully 3D shape as well as the fast processing time. Real-world evaluations with a static and dynamic vehicle show promising results compared to state-of-the-art 3D extended target tracking algorithms.

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An Orientation Corrected Bounding Box Fit Based on the Convex Hull under Real Time Constraints

Cited by 22 publications

References 20 publications

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

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

A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

Fast 3D Extended Target Tracking using NURBS Surfaces

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