CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure Traditional attempts for loop closure detection typically use handcrafted features, relying on geometric and visual information only, whereas more modern approaches tend to use semantic, appearance or geometric features extracted from deep convolutional neural networks (CNNs). While these approaches are successful in many applications, they do not utilize all of the information that a monocular image provides, and many of them, particularly the deeplearning based methods, require userchosen thresholding to actually close loops xe2x80x93 which may impact generality in practical applications. In this work, we address these issues by extracting all three modes of information from a custom deep CNN trained specifically for the task of place recognition. Our network is built upon a combination of a semantic segmentator, Variational Autoencoder (VAE) and triplet embedding network. The network is trained to construct a global feature space to describe both the visual appearance and semantic layout of an image. Then local keypoints are extracted from maximallyactivated regions of lowlevel convolutional feature maps, and keypoint descriptors are extracted from these feature maps in a novel way that incorporates ideas from successful handcrafted features. These keypoints are matched globally for loop closure candidates, and then used as a final geometric check to refute false positives. As a result, the proposed loop closure detection system requires no touchy thresholding, and is highly robust to false positives xe2x80x93 achieving better precisionrecall curves than the stateoftheart NetVLAD, and with realtime speeds.

Fast and Incremental Loop Closure Detection Using Proximity Graphs Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bagofwords (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled FILDxe2x80x99 (Fast and Incremental Loop closure Detection), which focuses on an online and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural Networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for realtime geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100% precision compared to other stateoftheart methods. The source code can be downloaded at https:github.comlAnshanTJUFILD for further studies.