Loop closure detection is the process of detecting whether an agent has returned to a previously visited location.
( Image credit: Backtracking Regression Forests for Accurate Camera Relocalization )
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM).
For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.
In recent years, methods concerning the place recognition task have been extensively examined from the robotics community within the scope of simultaneous localization and mapping applications.
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
To this end, the novel kernel learning methods for several basic visual perceptual tasks, including object tracking, localization, mapping, and image recognition, are proposed and demonstrated both theoretically and practically.
Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.
Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics.
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Combined with high-level semantics, Sem-LS is more robust under cluttered environment compared with existing line-shaped representations.
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest.