Deep 3D Object Detection Networks Using LiDAR Data: A Review

Wu, Yutian; Wang, Yueyu; Zhang, Shuwei; Ogai, Harutoshi

doi:10.1109/jsen.2020.3020626

Cited by 124 publications

(45 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, some of the most recent smartphones support sensors that acquire LiDAR (light detection and ranging) data, and while this is currently a device-specific feature, given the rapid pace of technological development, we would expect it to be included in many future smartphones. Thus, LiDAR data can be a potential data type obtained in citizen science projects, and although some studies have been performed to identify objects from point clouds using deep learning [83,84], applying such techniques to LiDAR data collected by citizen scientists is a very interesting challenge towards the combination of ML and citizen science.…”

Section: Future Challenges and Conclusionmentioning

confidence: 99%

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

2021

View full text Add to dashboard Cite

Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.

show abstract

Section: Future Challenges and Conclusionmentioning

confidence: 99%

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

2021

View full text Add to dashboard Cite

show abstract

“…In recent years, various deep learning methods [11,12] have continuously refreshed the detection accuracy rankings for the KITTI dataset [13]. However, these methods have still not been able to eliminate dependence on the dataset [14]. Better application effects necessitate ever-increasing reliance on the large-scale dataset.…”

Section: Of 30mentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…existing techniques [38,36,19] for images or LiDAR are employed to obtain preliminary detections. Radar data, including radial velocity, once associated with the initial detections, are used as additional cues to predict full velocities of objects.…”

Section: Velocity Estimation In Perception Systemsmentioning

confidence: 99%

Full-Velocity Radar Returns by Radar-Camera Fusion

Long

Morris

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.

show abstract

Deep 3D Object Detection Networks Using LiDAR Data: A Review

Cited by 124 publications

References 82 publications

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

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

Full-Velocity Radar Returns by Radar-Camera Fusion

Contact Info

Product

Resources

About