Fast color/texture segmentation for outdoor robots

We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.

Authors

• Morten Rufus Blas
• Motilal Agrawal
• Aravind Sundaresan
• Kurt Konolige

Keywords

• Image segmentation
• Image color analysis
• Histograms
• Pixel
• Robots
• Clustering algorithms
• Filter bank
• Outdoor Robot
• Segmentation Algorithm
• Textual Descriptions
• Local Texture
• Vector-based
• Ground Plane
• Segmentation Results
• Local Neighborhood
• K-means Algorithm
• Segmentation Map
• Pixel Location
• Color Information
• Small Neighborhood
• Local Descriptors
• Mean Width
• Stereopsis
• Set Of Segments
• Integral Image
• Inliers
• Combination Of Segmentation
• 2D Grid
• Tallgrass
• Terrain Types
• Unstructured Environments
• Simple Profile
• University Of Southern California