Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction

In visual place recognition (VPR), filtering and sequence-based matching approaches can improve performance by integrating temporal information across image sequences, especially in challenging conditions. While these methods are commonly applied, their effects on system behavior can be unpredictable and can actually make performance worse in certain situations. In this work, we present a new supervised learning approach that learns to predict the per-frame sequence matching receptiveness (SMR) of VPR techniques, enabling the system to selectively decide when to trust the output of a sequence matching system. The approach is agnostic to the underlying VPR technique. Our approach predicts SMR-and hence significantly improves VPR performance-across a large range of state-of-the-art and classical VPR techniques (namely CosPlace, MixVPR, EigenPlaces, SALAD, AP-GeM, NetVLAD and SAD), and across three benchmark VPR datasets (Nordland, Oxford RobotCar, and SFU-Mountain). We also provide insights into a complementary approach that uses the predictor to replace discarded matches, as well as ablation studies, including an analysis of the interactions between our SMR predictor and the selected sequence length. We will release our code upon acceptance.

Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments

Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional visual localization methods. We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR Benchmark-the first large-scale underwater VPR benchmark designed to leverage an extensive collection of unstructured data from multiple robotic platforms, spanning time intervals from days to years. The dataset encompasses diverse trajectories, arbitrary overlap and diverse seafloor types captured under varying environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc

A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition

Visual Place Recognition (VPR) enables coarse localization by comparing query images to a reference database of geo-tagged images. Recent breakthroughs in deep learning architectures and training regimes have led to methods with improved robustness to factors like environment appearance change, but with the downside that the required training and/or matching compute scales with the number of distinct environmental conditions encountered. Here, we propose Hyperdimensional One Place Signatures (HOPS) to simultaneously improve the performance, compute and scalability of these state-of-the-art approaches by fusing the descriptors from multiple reference sets captured under different conditions. HOPS scales to any number of environmental conditions by leveraging the Hyperdimensional Computing framework. Extensive evaluations demonstrate that our approach is highly generalizable and consistently improves recall performance across all evaluated VPR methods and datasets by large margins. Arbitrarily fusing reference images without compute penalty enables numerous other useful possibilities, three of which we demonstrate here: descriptor dimensionality reduction with no performance penalty, stacking synthetic images, and coarse localization to an entire traverse or environmental section.

Look Ma, No Ground Truth! Ground-Truth-Free Tuning of Structure from Motion and Visual SLAM

Evaluation is critical to both developing and tuning Structure from Motion (SfM) and Visual SLAM (VSLAM) systems, but is universally reliant on high-quality geometric ground truth -- a resource that is not only costly and time-intensive but, in many cases, entirely unobtainable. This dependency on ground truth restricts SfM and SLAM applications across diverse environments and limits scalability to real-world scenarios. In this work, we propose a novel ground-truth-free (GTF) evaluation methodology that eliminates the need for geometric ground truth, instead using sensitivity estimation via sampling from both original and noisy versions of input images. Our approach shows strong correlation with traditional ground-truth-based benchmarks and supports GTF hyperparameter tuning. Removing the need for ground truth opens up new opportunities to leverage a much larger number of dataset sources, and for self-supervised and online tuning, with the potential for a data-driven breakthrough analogous to what has occurred in generative AI.

Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications

Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis. Computer vision methods such as semantic segmentation aid automated image analysis, but typically rely on fully supervised training with extensive labelled data. While ground truth label masks for tasks like street scene segmentation can be quickly and affordably generated by non-experts through crowdsourcing services like Amazon Mechanical Turk, ecology presents greater challenges. The complexity of underwater images, coupled with the specialist expertise needed to accurately identify species at the pixel level, makes this process costly, time-consuming, and heavily dependent on domain experts. In recent years, some works have performed automated analysis of underwater imagery, and a smaller number of studies have focused on weakly supervised approaches which aim to reduce the expert-provided labelled data required. This survey focuses on approaches which reduce dependency on human expert input, while reviewing the prior and related approaches to position these works in the wider field of underwater perception. Further, we offer an overview of coastal ecosystems and the challenges of underwater imagery. We provide background on weakly and self-supervised deep learning and integrate these elements into a taxonomy that centres on the intersection of underwater monitoring, computer vision, and deep learning, while motivating approaches for weakly supervised deep learning with reduced dependency on domain expert data annotations. Lastly, the survey examines available datasets and platforms, and identifies gaps, barriers, and opportunities for automating underwater surveys.

Exploring Emerging Trends and Research Opportunities in Visual Place Recognition

Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localization implementations or re-localization and loop closure detection pipelines within simultaneous localization and mapping (SLAM). More specifically, it corresponds to the system's ability to identify and match a previously visited location using computer vision tools. Towards developing novel techniques with enhanced accuracy and robustness, while motivated by the success presented in natural language processing methods, researchers have recently turned their attention to vision-language models, which integrate visual and textual data.

ROS2WASM: Bringing the Robot Operating System to the Web

The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. This approach significantly enhances reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of a web platform available at www.ros2wasm.dev that allows users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.

Matched Filtering based LiDAR Place Recognition for Urban and Natural Environments

Place recognition is an important task within autonomous navigation, involving the re-identification of previously visited locations from an initial traverse. Unlike visual place recognition (VPR), LiDAR place recognition (LPR) is tolerant to changes in lighting, seasons, and textures, leading to high performance on benchmark datasets from structured urban environments. However, there is a growing need for methods that can operate in diverse environments with high performance and minimal training. In this paper, we propose a handcrafted matching strategy that performs roto-translation invariant place recognition and relative pose estimation for both urban and unstructured natural environments. Our approach constructs Birds Eye View (BEV) global descriptors and employs a two-stage search using matched filtering -- a signal processing technique for detecting known signals amidst noise. Extensive testing on the NCLT, Oxford Radar, and WildPlaces datasets consistently demonstrates state-of-the-art (SoTA) performance across place recognition and relative pose estimation metrics, with up to 15% higher recall than previous SoTA.

MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that current UDA methods show inferior results on fine structures and tend to oversegment objects with ambiguous appearance. To address these shortcomings, we propose to leverage geometric information, i.e., depth predictions, as depth discontinuities often coincide with segmentation boundaries. We show that naively incorporating depth into current UDA methods does not fully exploit the potential of this complementary information. To this end, we present MICDrop, which learns a joint feature representation by masking image encoder features while inversely masking depth encoder features. With this simple yet effective complementary masking strategy, we enforce the use of both modalities when learning the joint feature representation. To aid this process, we propose a feature fusion module to improve both global as well as local information sharing while being robust to errors in the depth predictions. We show that our method can be plugged into various recent UDA methods and consistently improve results across standard UDA benchmarks, obtaining new state-of-the-art performances.

RMMI: Enhanced Obstacle Avoidance for Reactive Mobile Manipulation using an Implicit Neural Map

We introduce RMMI, a novel reactive control framework for mobile manipulators operating in complex, static environments. Our approach leverages a neural Signed Distance Field (SDF) to model intricate environment details and incorporates this representation as inequality constraints within a Quadratic Program (QP) to coordinate robot joint and base motion. A key contribution is the introduction of an active collision avoidance cost term that maximises the total robot distance to obstacles during the motion. We first evaluate our approach in a simulated reaching task, outperforming previous methods that rely on representing both the robot and the scene as a set of primitive geometries. Compared with the baseline, we improved the task success rate by 25% in total, which includes increases of 10% by using the active collision cost. We also demonstrate our approach on a real-world platform, showing its effectiveness in reaching target poses in cluttered and confined spaces using environment models built directly from sensor data. For additional details and experiment videos, visit https://rmmi.github.io/.