Surface-type classification in structured planar environments under various illumination and imaging conditions

Publication Type:
Issue Date:
Full metadata record
The recent advancement in sensing, computing and artificial intelligence, has led to the application of robots outside of the manufacturing factory and into field environments. In order for a field robot to operate intelligently and autonomously, the robot needs to build an environmental awareness, such as by classifying the different surface-types on a steel bridge structure. However, it is challenging to classify surface-types from images that are captured in a structurally complex environment under various illumination and imaging conditions. This is because colour and texture features extracted from these images can be inconsistent. This thesis presents a surface-type classification approach to classify surface-types in a structurally complex three-dimensional (3D) environment under various illumination and imaging conditions. The approach proposes RGB-D sensing to provide each pixel in an image with additional depth information that is used by two developed algorithms. The first algorithm uses the RGB-D information along with a modified reflectance model to extract colour features for colour-based classification of surface-types. The second algorithm uses the depth information to calculate a probability map for the pixels being a specific surface-type. The probability map can identify the image regions that have a high probability of being accurately classified by a texture-based classifier. A 3D grid-based map is generated to combine the results produced by colour-based classification and texture-based classification. It is suggested that a robot manipulator is used to position an RGB-D sensor package in the complex environments to capture the RGB-D images. In this way, the 3D position of each pixel is precisely known in a common global frame (robot base coordinate frame) and can be combined using a grid-based map to build up a rich awareness of the surrounding complex environment. A case study is conducted in a laboratory environment using a six degree-of-freedom robot manipulator equipped with a RGB-D sensor package mounted to the end effector. The results show that the proposed surface-type classification approach provides an improved solution for vision-based classification of surface-types in a complex structural environment with various illumination and imaging conditions.
Please use this identifier to cite or link to this item: