Lidar–Camera Semi-Supervised Learning for Semantic Segmentation

Caltagirone, Luca; Bellone, Mauro; Svensson, Lennart; Wahde, Mattias; Sell, Raivo

doi:10.3390/s21144813

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…Camera data is fused with LiDAR data in order to detect better objects [ 26 ]. In some works, the detection of objects is approached by performing semantic segmentation on LiDAR data [ 29 , 30 ] or camera-LiDAR fused data [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

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In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

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Section: Related Workmentioning

confidence: 99%

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

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“…Besides, different Lidars can work cooperatively to better monitor traffic situations (Kloeker et al, 2020). Even when Lidars are fused with radars or cameras, Lidars are the fundamental sensor for positioning traffic agents (Caltagirone et al, 2019). Therefore, this study emphasizes Lidar sensors as RSUs.…”

Section: Introductionmentioning

confidence: 99%

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Ma,

Zheng,

Wang

et al. 2024

Computer aided Civil Eng

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Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real‐life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual‐real‐fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real‐world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure‐retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM‐based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM‐based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM‐based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches.

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“…More importantly, latefusion systems incorporate single-modality detectors. Therefore, our method projects point clouds into the camera plane to create a three-channel tensor with the same width and height of the image, of which each channel encodes one of the 3D spatial coordinates [9].…”

Section: Introductionmentioning

confidence: 99%

Object Segmentation for Autonomous Driving Using iseAuto Data

Bellone

Sell

et al. 2022

Electronics

Self Cite

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Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy weather. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

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Lidar–Camera Semi-Supervised Learning for Semantic Segmentation

Cited by 15 publications

References 25 publications

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Object Segmentation for Autonomous Driving Using iseAuto Data

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