Object Dimension Extraction for Environment Mapping with Low Cost Cameras Fused with Laser Ranging
This work addresses environment mapping for applications like exploration and disaster relief, but it appears incremental as it combines existing techniques with a new noise reduction method.
The paper tackled the problem of mapping unknown terrain by extracting object dimensions using a fusion of stereo cameras and laser ranging, achieving noise reduction through a novel dilation-based method on disparity maps.
It is essential to have a method to map an unknown terrain for various applications. For places where human access is not possible, a method should be proposed to identify the environment. Exploration, disaster relief, transportation and many other purposes would be convenient if a map of the environment is available. Replicating the human vision system using stereo cameras would be an optimum solution. In this work, we have used laser ranging based technique fused with stereo cameras to extract dimension of objects for mapping. The distortions were calibrated using mathematical model of the camera. By means of Semi Global Block Matching [1] disparity map was generated and reduces the noise using novel noise reduction method of disparity map by dilation. The Data from the Laser Range Finder (LRF) and noise reduced vision data has been used to identify the object parameters.