Abnormal Occupancy Grid Map Recognition using Attention Network

• URL: http://arxiv.org/abs/2110.09047v1
• Date: Mon, 18 Oct 2021 06:37:21 GMT
• Title: Abnormal Occupancy Grid Map Recognition using Attention Network
• Authors: Fuqin Deng, Hua Feng, Mingjian Liang, Qi Feng, Ningbo Yi, Yong Yang, Yuan Gao, Junfeng Chen, and Tin Lun Lam
• Abstract summary: This work focuses on automatic abnormal occupancy grid map recognition using the residual neural networks and a novel attention mechanism module. We propose an effective channel and spatial Residual SE(csRSE) attention module, which contains a residual block for producing hierarchical features. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.
• Score: 13.145725469711413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural networks and a novel attention mechanism module. We propose an effective channel and spatial Residual SE(csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiment with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.

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