Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
arXiv:1703.03613
Abstract
In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.
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Cited by in corpus (8)
- SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
- DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
- LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR
- LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
- Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
- A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications
- LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
- Deep Multi-Sensor Lane Detection