An efficient curb detection and tracking method for intelligent vehicles via a high-resolution 3D-LiDAR

Jie, Haoxiang; Gao, Xin; Zhao, Qixi; Ning, Zuotao; Hu, Jun; Liu, Lifeng; Liu, Wei

doi:10.1117/12.2639520

Cited by 4 publications

(3 citation statements)

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“…In [28], feature points are extracted through image segmentation and energy minimization, followed by the application of principal curves and surfaces methods in [29] for fitting detected curbs. Studies like [14], [15], [30], [31] utilize the horizontal and vertical continuity of point clouds, employing angle and height thresholds for curb feature extraction and using Gaussian Process Regression (GPR) and Random Sample Consensus for curb curve fitting. [15], [16]integrate the generalized curvature method from LOAM [23] into curb detection, refining the process with Gaussian Process Regression.…”

Section: Related Work a Manual Feature Extraction Methodsmentioning

confidence: 99%

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

et al. 2022

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3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot efficiently capture large amounts of context information, resulting in limited receptive fields and limiting their performance. To address this problem, a sparse voxel-based attention network is introduced for 3D LiDAR point cloud semantic segmentation, termed SVASeg, which captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA). The traditional multi-head attention cannot directly be applied to the non-empty sparse voxels. To this end, a hash table is built according to the incrementation of voxel coordinates to lookup the non-empty neighboring voxels of each sparse voxel. Then, the sparse voxels are grouped into different groups, and each group corresponds to a local region. Afterwards, position embedding, multi-head attention and feature fusion are performed for each group to capture and aggregate the context information. Based on the SMHA module, the SVASeg can directly operate on the non-empty voxels, maintaining a comparable computational overhead to the convolutional method. Extensive experimental results on the SemanticKITTI and nuScenes datasets show the superiority of SVASeg.

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Section: Related Work a Manual Feature Extraction Methodsmentioning

confidence: 99%

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

et al. 2022

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“…It is obvious that detection by multi-sensor fusion needs accurate extrinsic calibration between sensors. However, there are unavoidable vibrations in real autonomous driving scenarios, leading to biases in extrinsic parameters [48]. Thus, in this subsection, we conduct an experiment to analyze the impact of calibration errors at different levels.…”

Section: B Influence From Calibration Errormentioning

confidence: 99%

G-Fusion: LiDAR and Camera Feature Fusion on the Ground Voxel Space

Cheng,

Ning,

et al. 2024

IEEE Access

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Detection based on LiDAR and camera fusion is increasingly popular for researchers in the autonomous driving domain. Compared to the camera-only and LiDAR-only methods, the fusion-based methods indeed improve the detection accuracy on public-available datasets. However, due to the complexity of the projection or fusion mechanism, few of these methods can run in real-time even on an advanced desktop GPU. Thus, in this paper, we propose a new fusion detection model G-Fusion with a light and fast image view-transform module. According to our receptive field analysis of image feature maps, we directly project image features to only one voxel layer located on the ground, then simply fuse the LiDAR and image features by concatenation and convolution. With this delicately designed module, G-Fusion greatly boosts the state-of-the-art speed performance on the nuScenes dataset, achieving a good balance with the competitive detection scores. Meanwhile, since the precision of sensor extrinsic parameters is important for most fusion-based methods, we also deeply dig into our model's calibration error tolerance ability and discover the failure noise condition.

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“…The Transformer model has an exceptional capability to extract both global and local information, rendering it suitable for various tasks as a plug-and-play module. Transformer-based LLEs, such as Retinexformer [29], Uformer [30], Restormer [31], LL-Former [32], and LYT-Net [33] have demonstrated significant accuracy advantages. However, Transformers demand substantial computational and memory resources, which is unsuitable for low-performance devices.…”

Section: Transformer-based Modelmentioning

confidence: 99%

A 5K Efficient Low-Light Enhancement Model by Estimating Increment between Dark Image and Transmission Map Based on Local Maximum Color Value Prior

Deng,

Choo,

et al. 2024

Electronics

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Low-light enhancement (LLE) has seen significant advancements over decades, leading to substantial improvements in image quality that even surpass ground truth. However, these advancements have come with a downside as the models grew in size and complexity, losing their lightweight and real-time capabilities crucial for applications like surveillance, autonomous driving, smartphones, and unmanned aerial vehicles (UAVs). To address this challenge, we propose an exceptionally lightweight model with just around 5K parameters, which is capable of delivering high-quality LLE results. Our method focuses on estimating the incremental changes from dark images to transmission maps based on the low maximum color value prior, and we introduce a novel three-channel transmission map to capture more details and information compared to the traditional one-channel transmission map. This innovative design allows for more effective matching of incremental estimation results, enabling distinct transmission adjustments to be applied to the R, G, and B channels of the image. This streamlined approach ensures that our model remains lightweight, making it suitable for deployment on low-performance devices without compromising real-time performance. Our experiments confirm the effectiveness of our model, achieving high-quality LLE comparable to the IAT (local) model. Impressively, our model achieves this level of performance while utilizing only 0.512 GFLOPs and 4.7K parameters, representing just 39.1% of the GFLOPs and 23.5% of the parameters used by the IAT (local) model.

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An efficient curb detection and tracking method for intelligent vehicles via a high-resolution 3D-LiDAR

Cited by 4 publications

References 0 publications

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

G-Fusion: LiDAR and Camera Feature Fusion on the Ground Voxel Space

A 5K Efficient Low-Light Enhancement Model by Estimating Increment between Dark Image and Transmission Map Based on Local Maximum Color Value Prior

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