Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
This addresses the problem of efficient loop closure detection in SLAM systems for robotics and autonomous navigation, though it is incremental as it builds on existing pre-trained models.
The paper tackles the challenge of deploying previously trained deep learning models for visual place recognition in new geographical regions without extensive retraining, achieving comparable image retrieval performance with a low 512-D descriptor compared to high 512-D descriptors in state-of-the-art methods.
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This paper addresses the problem of their deployment with simultaneous localization and mapping (SLAM) systems in the new environment. The general baseline approach uses additional information, such as GPS, sequential keyframes tracking, and re-training the whole environment to enhance the recall rate. We propose a novel approach for improving image retrieval based on previously trained models. We present an intelligent method, MAQBOOL, to amplify the power of pre-trained models for better image recall and its application to real-time multiagent SLAM systems. We achieve comparable image retrieval results at a low descriptor dimension (512-D), compared to the high descriptor dimension (4096-D) of state-of-the-art methods. We use spatial information to improve the recall rate in image retrieval on pre-trained models.