ECA-SLAM: A Visual SLAM Utilizing Depth Features and Attention Mechanism

Simultaneous Localization and Mapping (SLAM) has a broad application prospect in robot navigation, autonomous driving, and augmented reality. While visual SLAM systems have made significant progress, they still face challenges in complex environments due to the complexity and time variability of the real environment. Traditional methods have limitations in feature extraction, matching, and mapping, and the effect of localization and mapping can be greatly reduced due to scene changes. In response to these challenges, this paper proposes an improved visual SLAM method based on deep learning. We use a lightweight Mobilenetv2 network for feature extraction and introduce the Hardswish activation function to improve the performance further. Meanwhile, the ECA-Net attention mechanism is introduced into the feature extraction network to enhance its ability to capture key image features accurately. In addition, we use multi-task distillation to train our network to reduce the complexity of the model. Extensive experimental evaluation on multiple datasets reveals that the proposed method has significantly improved accuracy and robustness, particularly in challenging environments, demonstrating superior performance. Index Terms—SLAM, ECA-Net, deep learning, feature extraction