Abnormal Occupancy Grid Map Recognition using Attention Network

TL;DR

This paper introduces an attention-based neural network with a novel csRSE module for automatic recognition of abnormal occupancy grid maps, significantly improving accuracy in autonomous navigation systems.

Contribution

It proposes a new attention mechanism (csRSE) and constructs a dedicated dataset (OGMD) for effective abnormal occupancy grid map recognition.

Findings

Achieved 96.23% accuracy on OGMD test dataset.

Outperformed other attention mechanisms in recognition tasks.

Demonstrated the effectiveness of the csRSE module in feature extraction.

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.