12 research outputs found
GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition
The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method—Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2–5%, the graph construction method improves the accuracy of graph classification by approximately 4–6%, and that the whole Visual Place Recognition model is robust to appearance change and view change
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
Where am I? This is one of the most critical questions that any intelligent
system should answer to decide whether it navigates to a previously visited
area. This problem has long been acknowledged for its challenging nature in
simultaneous localization and mapping (SLAM), wherein the robot needs to
correctly associate the incoming sensory data to the database allowing
consistent map generation. The significant advances in computer vision achieved
over the last 20 years, the increased computational power, and the growing
demand for long-term exploration contributed to efficiently performing such a
complex task with inexpensive perception sensors. In this article, visual loop
closure detection, which formulates a solution based solely on appearance input
data, is surveyed. We start by briefly introducing place recognition and SLAM
concepts in robotics. Then, we describe a loop closure detection system's
structure, covering an extensive collection of topics, including the feature
extraction, the environment representation, the decision-making step, and the
evaluation process. We conclude by discussing open and new research challenges,
particularly concerning the robustness in dynamic environments, the
computational complexity, and scalability in long-term operations. The article
aims to serve as a tutorial and a position paper for newcomers to visual loop
closure detection.Comment: 25 pages, 15 figure