Place recognition using kernel visual keyword descriptors

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.

Visual Place Recognition (VPR) is essential for robotics and autonomous navigation, yet most methods rely on heavy task-specific training. Existing approaches fall into two main paradigms: single-stage models that learn compact global descriptors, and two-stage pipelines that combine coarse global retrieval with local feature or geometric verification. While effective, both require large annotated datasets and carefully tuned optimization, limiting scalability and cross-domain reuse. We introduce TF-VPR, a new benchmark that tackles a more challenging setting: VPR performed entirely without additional training, where descriptors are generated, refined and matched only at test time. Enabled by recent Vision Foundation Models (VFMs), TF-VPR systematically evaluates how far pretrained VFMs can be pushed for place recognition when used as-is, and provides a standardized protocol for fairly comparing arbitrary VFMs without fine-tuning. To support this, we unify major VPR datasets covering diverse real-world conditions and propose two lightweight, training-free modules: Training-Free Graph-Attention Graph Module (TF-GAM) and Training-Free Cross-Attention Module (TF-CAM). These plug-and-play modules enhance descriptor discriminability and retrieval robustness. Experiments show that TF-VPR exposes new challenges and reveals previously unexplored strengths of VFMs for training-free place recognition. Code and datasets are available at https://github.com/ddfs430/TF-VPR .

Visual place recognition is a challenging task for applications such as autonomous driving navigation and mobile robot localization. Distracting elements presenting in complex scenes often lead to deviations in the perception of visual place. To address this problem, it is crucial to integrate information from only task-relevant regions into image representations. In this paper, we introduce a novel holistic place recognition model, TransVPR, based on vision Transformers. It benefits from the desirable property of the self-attention operation in Transformers which can naturally aggregate task-relevant features. Attentions from multiple levels of the Transformer, which focus on different regions of interest, are further combined to generate a global image representation. In addition, the output tokens from Transformer layers filtered by the fused attention mask are considered as key-patch descriptors, which are used to perform spatial matching to re-rank the candidates retrieved by the global image features. The whole model allows end-to-end training with a single objective and image-level supervision. TransVPR achieves state-of-the-art performance on several real-world benchmarks while maintaining low computational time and storage requirements.

Visual place recognition is the problem of camera-based localization of a robot given a database of images of known places, potentially under severe appearance changes (e.g., different weather or illumination). Typically, the current query images and the database images are sequences of consecutive images. In previous work, we proposed to adapt hierarchical temporal memory, a biologically plausible model of sequence processing in the human neocortex to address this task. The previous work described the algorithmic steps and showed synthetic experimental results from simulation. This letter extends the approach to application on real-world data based on a novel encoder for state-of-the-art image processing front ends. The neurologically inspired approach is compared with several state-of-the-art algorithms on a variety of datasets and shows preferable performance. Beyond the place recognition performance, the neurological roots of the algorithm result in appealing properties like potentially very energy efficient implementation due to the usage of sparse distributed representations and natural extendability like the integration of motion estimates similar to entorhinal grid cells. Finally, we underline its practical applicability by online, soft real-time application on a mobile robot.

Appearance-invariant place recognition by discriminatively training a convolutional neural network

Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n3) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM.

The mobile welding robot based on a hybrid power drive system is a system with complex control characteristics such as nonlinearity, strong coupling, and disturbance of working environment. These characteristics have different effects on the control performance. In order to improve control characteristics of the mobile welding robot, it is necessary to study an effective evaluation method to explore the influence of these parameters on the control performance of the mobile welding robot. In the process of seam tracking, the operation indexes of welding robot system are determined by comprehensive evaluation. On the premise of ensuring tracking accuracy and stability, the energy distribution of hybrid power system is optimized to improve results of continuous power supply time. Therefore, a comprehensive evaluation of the welding robot is established, which is combined information entropy and Kernel Principal Component Analysis (KPCA). The evaluation parameters are divided into the first-level evaluation index and the second-level evaluation index. The evaluation model is established by using information entropy, and the data are linearized and dimensionally reduced by using the KPCA. The results show that the method can well deal with and analyze the nonlinear relationship between various indexes. At the same time, the numerical relationship between performance indexes can be established. On this basis, a comprehensive performance evaluation theory based on information entropy, and nuclear principal components is established. Finally, the comprehensive evaluation results are brought into the energy management optimization strategy. The experimental results show that this method not only improves the accuracy of weld tracking, but also ensures the stability of the robot and reduces energy consumption.

Visual place recognition: A survey from deep learning perspective

Omnidirectional cameras are a suitable and cost-effective choice for Visual Place Recognition (VPR), as they provide comprehensive information from the scene regardless of the robot orientation. However, vision sensors are vulnerable to environmental appearance changes (e.g., illumination, weather, season or moving objects). While multi-modal sensing approaches can overcome these challenges, they introduce significant cost and system complexity. This paper introduces PDPR (Panoramic-Depth Place Recognition), a novel fusion framework that enhances the robustness of VPR methods by integrating visual data with geometric features derived from monocular depth estimation techniques, while using a single-camera setup. In the ablation study, both early and late fusion strategies are evaluated to optimally combine appearance-based and depth-derived features. The extensive evaluation on challenging, indoor and outdoor datasets demonstrates that PDPR consistently boosts retrieval performance across multiple state-of-the-art VPR models. Furthermore, this improvement is achieved without requiring any fine tuning, allowing our method to function as a pluggable module for pretrained models. Consequently, this work presents a powerful, practical and low-cost solution for robust VPR, with high potential to scale as monocular depth estimation and VPR models continue to improve. The project website can be found at https://marcosalfaro.github.io/projects-PDPR/ . • Monocular depth estimation is used to enhance place recognition. • A thorough evaluation of preprocessing techniques to enhance the depth maps. • Fusion techniques are designed to leverage visual and geometric data. • A model-agnostic approach that improves the performance even with no fine tuning. • A robust method across different scenarios and lighting conditions.

Visual place recognition from a 3D laser LiDAR is one of the most active research areas in robotics. Especially, learning and recognition of scene descriptors, such as scan context descriptors that map 3D point clouds to 2D point clouds, is one of the promising research directions. Although the scan-context descriptor has a sufficiently high recognition performance, it is still expensive image data and cannot be handled with low-capacity non-deep models. In this paper, we explore the task of compressing the scan context descriptor model while maintaining its recognition performance. To this end, the proposed approach slightly modifies the off-the-shelf classifier model of convolutional neural networks (CNN) from its basis, by replacing the SoftMax part with a support vector machine (SVM). Experiments with publicly available NCLT dataset validate the effectiveness of the proposed approach.

Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, since the two types of features are usually processed independently, re-ranking may harm global retrieval, termed re-ranking confusion. Moreover, re-ranking is limited by global retrieval. In this paper, we propose a tightly coupled learning (TCL) strategy to train triplet models. Different from original triplet learning (OTL) strategy, it combines global and local descriptors for joint optimization. In addition, a bidirectional search dynamic time warping (BS-DTW) algorithm is also proposed to mine locally spatial information tailored to VPR in re-ranking. The experimental results on public benchmarks show that the models using TCL outperform the models using OTL, and TCL can be used as a general strategy to improve performance for weakly supervised ranking tasks. Further, our lightweight unified model is better than several state-of-the-art methods and has over an order of magnitude of computational efficiency to meet the real-time requirements of robots.

We present a novel data embedding that significantly reduces the estimation error of locality sensitive hashing (LSH) technique when used in reproducing kernel Hilbert space (RKHS). Efficient and accurate kernel approximation techniques either involve the kernel principal component analysis (KPCA) approach or the Nystrom approximation method. In this work we show that extant LSH methods in this space suffer from a bias problem, that moreover is difficult to estimate apriori. Consequently, the LSH estimate of a kernel is different from that of the KPCA/Nystrom approximation. We provide theoretical rationale for this bias, which is also confirmed empirically. We propose an LSH algorithm that can reduce this bias and consequently our approach can match the KPCA or the Nystrom methods’ estimation accuracy while retaining the traditional benefits of LSH. We evaluate our algorithm on a wide range of realworld image datasets (for which kernels are known to perform well) and show the efficacy of our algorithm using a variety of principled evaluations including mean estimation error, KL divergence and the Kolmogorov-Smirnov test.

Visual place recognition is a mature field in mobile robotics research. Recognizing places in datasets covering traversals of hundreds or thousands of kilometres and accurate localization in small and medium size environments have been successfully demonstrated. However, for real world long term operation, visual place recognition has to face severe environmental appearance changes due to day-night cycles, seasonal or weather changes. Existing approaches for recognizing places in such changing environments provide solutions for matching images from the exact same viewpoint using powerful holistic descriptors, using less sophisticated holistic descriptors in combinations with images sequences, and/or pose strong requirements on training data to learn systematic appearance changes. In this paper, we present a novel, training free, single image matching procedure that builds upon local region detectors for powerful Convolutional Neural Network (CNN) based descriptors. It can be used with a broad range of local region detectors including keypoints, segmentation based approaches and object proposals. We propose a novel evaluation criterion for selection of an appropriate local region detector for changing environments and compare several available detectors. The scale space extrema detector known from the SIFT keypoint detector in combination with appropriate magnification factors performs best. We present preliminary results of the proposed image matching procedure with several region detectors on the challenging Nordland dataset on place recognition between different seasons and a dataset including severe viewpoint changes. The proposed method outperforms the best existing holistic method for place recognition in such changing environments and can additionally handle severe viewpoint changes. Additionally, the combination of the best performing detectors with superpixel based spatial image support shows promising results.

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Spatial verification is a key step to remove outliers for accurate feature matching in visual place recognition. In this paper, we propose a novel method for outlier detection using a hierarchical spatial verification scheme. Given a set of putative correspondences between a pair of images, we convert the matching problem into a 4D transformation space and identify promising similarity transformations using Hough voting. In the hierarchical scheme, we first use a hypothesize-and-verify technique to identify groups of correspondences according to each similarity transformation. Second, the group with the largest number of correspondences serves as a standard to subsequently remove outliers in other groups by explicit geometric consistency checking. We have compared the proposed method with the state-of-the-art solutions on five popular public datasets to show that our method has better performance in place recognition and loop closure detection.