Abstract
New research on Robotics is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Generating an accurate and continuous semantic occupancy map is a key component of autonomous robotics. Most existing continuous semantic occupancy mapping methods neglect the potential differences between voxels, which reconstruct an overinflated map.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), CAST program, Collective Intelligence amp; Collaboration Laboratory. Our news journalists obtained a quote from the research from the Beijing Institute of Technology, “What is more, these methods have high computational complexity due to the fixed and large query range. To address the challenges of overinflation and inefficiency, this article proposes a novel sharp-edged and efficient continuous semantic occupancy mapping algorithm (SEE-CSOM). The main contribution of this work is to design the Redundant Voxel Filter Model (RVFM) and the Adaptive Kernel Length Model (AKLM) to improve the performance of the map. RVFM applies context entropy to filter out the redundant voxels with a low degree of confidence, so that the representation of objects will have accurate boundaries with sharp edges. AKLM adaptively adjusts the kernel length with class entropy, which reduces the amount of data used for training. Then, the multientropy kernel inference function is formulated to integrate the two models to generate the continuous semantic occupancy map.”
NETL
NSTL
IEEE