BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View

Barrera, Alejandro; Guindel, Carlos; Beltrán, Jorge Ramírez; García, Fernando

doi:10.1109/itsc45102.2020.9294293

Cited by 60 publications

(39 citation statements)

References 15 publications

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“…Due to the irregularities of the point cloud, many 3D detection algorithms convert the point cloud into 2D images to extract features in a 2D convolution manner and detect the method [18][19][20]. Zeng et al [21] proposed a Real-Time 3D (RT3D) algorithm for converting 3D point clouds into 2D grids.…”

Section: Lidarmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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Section: Lidarmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…The method has a low efficiency. Based on the framework, BirdNet+ [34] utilizes ad hoc regression branches to eliminate the need for a postprocessing stage. RT3D [35] also uses a 2D object detection method to achieve 3D detection, in which point cloud is projected into BEV (the channels are the maximum, average, and minimum height, respectively) and then R-FCN [36] is used to detect objects.…”

Section: Point Cloud-based 3d Object Detection Methodsmentioning

confidence: 99%

A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

Dai

Chen

Bao

et al. 2021

WEVJ

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In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception is the basis of intelligent planning and safe decision-making for intelligent vehicles, this paper presents a survey of the existing perceptual methods in vehicles, especially 3D object detection, which guarantees the reliability and safety of vehicles. In this review, we first introduce the role of perceptual module in autonomous driving system and a relationship with other modules. Then, we classify and analyze the corresponding perception methods based on the different sensors. Finally, we compare the performance of the surveyed works on public datasets and discuss the possible future research interests.

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“…With RGB images provided by visual camera, they contain sufficient semantic information and most detection algorithms and convert them through gray scale image [9]and 2D bounding box [10] to detection 3D object. For LiDAR point-cloud datas, some algorithms [11], [12] utilize unsupervised learning framework to achieve 3D object detection. Meanwhile, [13], [14] fusion segmented point-cloud to align with RGB image.…”

Section: A 3d Object Detectionmentioning

confidence: 99%

Monocular Semantic Mapping Based on 3D Cuboids Tracking

Gong

Miao

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Semantic mapping based on information of objects has become a crucial component for the surrounding comprehension and the more robust navigation. In this paper, we propose a system for simultaneous localization and mapping (SLAM) that combines multiple objects tracking and factor graph optimization with semantically meaningful landmarks to achieve accurate monocular semantic mapping. Firstly, the process of object recognition uses a vanishing point sampling-based approach to efficiently infer the class and position of object landmarks from 2D bounding box object detection. Secondly, The semantic frontend utilizes local matching-based data association to track raw cuboid proposals. It can provide semantic constraints to reduce cuboid scale drift and improve its position estimation. Finally, we present a multi-view factor graph optimization which can use motion modal of the camera to optimize stable cuboids. The semantic mapping experiments on our own built virtual scene show better accuracy and robustness over existing approaches. We evaluate the effectiveness of our approach on public KITTI datasets and a real scene.Index Terms-visual simultaneous localization and mapping (SLAM), 3D semantic map, factor graph optimization.

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BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View

Cited by 60 publications

References 15 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

Monocular Semantic Mapping Based on 3D Cuboids Tracking

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