Fast LIDAR-based road detection using fully convolutional neural networks

Caltagirone, Luca; Scheidegger, Samuel; Svensson, Lennart; Wahde, Mattias

doi:10.1109/ivs.2017.7995848

Cited by 202 publications

(136 citation statements)

References 23 publications

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“…Fong et al [10] also utilize DEM for the same purpose. Furthermore, Caltagirone et al [11] introduces a robust algorithm based on a convolutional neural network (CNN) to detect road surface and traversable areas from bird's-eye view maps. This shows good performance in terms of both speed and accuracy.…”

Section: A Road Curb Detectionmentioning

confidence: 99%

Road Curb Detection Using A Novel Tensor Voting Algorithm

Zhu

Han

Xue

et al. 2019

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Road curb detection is very important and necessary for autonomous driving because it can improve the safety and robustness of robot navigation in the outdoor environment. In this paper, a novel road curb detection method based on tensor voting is presented. The proposed method processes the dense point cloud acquired using a 3D LiDAR. Firstly, we utilize a sparse tensor voting approach to extract the line and surface features. Then, we use an adaptive height threshold and a surface vector to extract the point clouds of the road curbs. Finally, we utilize the height threshold to segment different obstacles from the occupancy grid map. This also provides an effective way of generating high-definition maps. The experimental results illustrate that our proposed algorithm can detect road curbs with near real-time performance.

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Section: A Road Curb Detectionmentioning

confidence: 99%

Road Curb Detection Using A Novel Tensor Voting Algorithm

Zhu

Han

Xue

et al. 2019

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…In addition, Han et al proposed a semisupervised learning road detection using generative adversarial networks(GANs) to overcome insufficient training data in fully supervised learning schemes and achieved th state-of-the-art performance on KITTI ROAD benchmark [1].Deep learn method also has a lot of practice in LiDAR based road detection. Luca et al in [2] project LiDAR point to a top-view to create grid maps, with which a FCN can be used to perform fast pixel-wise road detection. This work is a top-performing algorithm on KITTI ROAD using LiDAR.…”

Section: Related Workmentioning

confidence: 99%

“…It is easy to extract accurate metric and 3D spacial information from LiDAR results but point cloud is too sparse to support detection or segmentation tasks at a far distance. Methods using either of those two kinds of sensors have been widely studied in previous works [1,2,6,7,53,54,55,56]. However, to make autonomous vehicles smarter, researchers are trying to combine two sensors to draw on each other's strength.…”

mentioning

confidence: 99%

Deep representation learning for road detection using Siamese network

Liu

Han

et al. 2018

Multimed Tools Appl

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Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI ROAD demonstrate the effectiveness of our proposed approach.

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“…arXiv:1907.01294v2 [cs.CV] 17 Jul 2019 infer high-level information relative to lanes. Some of them process LiDAR data to exploit differences in lane markings reflectivity [1,2]. However, LiDARs are extremely expensive, thus not always available on a vehicle.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection and Classification Using Cascaded CNNs

Pizzati

Allodi

Barrera

et al. 2020

Lecture Notes in Computer Science

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Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.

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Fast LIDAR-based road detection using fully convolutional neural networks

Cited by 202 publications

References 23 publications

Road Curb Detection Using A Novel Tensor Voting Algorithm

Road Curb Detection Using A Novel Tensor Voting Algorithm

Deep representation learning for road detection using Siamese network

Lane Detection and Classification Using Cascaded CNNs

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