Non-flat Road Detection Based on A Local Descriptor

09/27/2016 ∙ by Kangru Wang, et al. ∙ 0 ∙ share

The detection of road and free space remains challenging for non-flat plane, especially with the varying latitudinal and longitudinal slope or in the case of multi-ground plane. In this paper, we propose a framework of the ground plane detection with stereo vision. The main contribution of this paper is a newly proposed descriptor which is implemented in the disparity image to obtain a disparity texture image. The ground plane regions can be distinguished from their surroundings effectively in the disparity texture image. Because the descriptor is implemented in the local area of the image, it can address well the problem of non-flat plane. And we also present a complete framework to detect the ground plane regions base on the disparity texture image with convolutional neural network architecture.

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Non-flat Road Detection Based on A Local Descriptor

Authors

Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network

A Machine Learning Approach to Recovery of Scene Geometry from Images

A Complete System for Candidate Polyps Detection in Virtual Colonoscopy

Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

GroundNet: Segmentation-Aware Monocular Ground Plane Estimation with Geometric Consistency

Low-level Vision by Consensus in a Spatial Hierarchy of Regions

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