Author name cluster

Alejandro Fontán

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

IROS Conference 2025 Conference Paper

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

Beverley Gorry
Tobias Fischer 0001
Michael Milford
Alejandro Fontán

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 images extracted from video sequences. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR BenchmarkÐthe 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 with varying overlap and diverse seafloor types captured under different environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc.

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IROS Conference 2025 Conference Paper

VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets

Alejandro Fontán
Tobias Fischer 0001
Javier Civera 0001
Michael Milford

Visual Simultaneous Localization and Mapping (VSLAM) research faces significant challenges due to fragmented toolchains, complex system configurations, and inconsistent evaluation methodologies. To address these issues, we present VSLAM-LAB, a unified framework designed to streamline the development, evaluation, and deployment of VSLAM systems. VSLAM-LAB simplifies the entire workflow by enabling seamless compilation and configuration of VSLAM algorithms, automated dataset downloading and preprocessing, and standardized experiment design, execution, and evaluation. All of these features are accessible through a single command-line interface. The framework supports a wide range of VSLAM systems and datasets, offering broad compatibility and extendability while promoting reproducibility through consistent evaluation metrics and analysis tools. By reducing implementation complexity and minimizing configuration overhead, VSLAM-LAB empowers researchers to focus on advancing VSLAM methodologies and accelerates progress toward scalable, real-world solutions. We demonstrate the ease with which user-relevant benchmarks can be created: here, we introduce difficulty-level-based categories, but one could envision environment-specific or condition-specific categories.

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ICRA Conference 2024 Conference Paper

Adaptive Outlier Thresholding for Bundle Adjustment in Visual SLAM

Alejandro Fontán
Javier Civera 0001
Michael Milford

State-of-the-art V-SLAM pipelines utilize robust cost functions and outlier rejection techniques to remove incorrect correspondences. However, these methods are typically fine-tuned to overfit certain benchmarks and struggle to adapt effectively to changes in the application domain or environmental conditions. This renders them impractical for many robotic applications in which robustness in a wide variety of conditions is essential. In this paper we introduce a novel distribution-based approach for online outlier rejection that reduces the necessity for scene-specific fine-tuning while simultaneously improving the overall SLAM performance. Through experiments across 3 different public datasets, we show that our approach consistently outperforms state-of-the-art methods in various real-world settings. Our code is available at https://github.com/alejandrofontan/ORB_SLAM2_Distribution

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ICRA Conference 2021 Conference Paper

DOT: Dynamic Object Tracking for Visual SLAM

Irene Ballester
Alejandro Fontán
Javier Civera 0001
Klaus H. Strobl
Rudolph Triebel

In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic environments. DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects in order to allow SLAM systems based on rigid scene models to avoid such image areas in their optimizations. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. This short-term tracking improves the accuracy of the segmentation with respect to other approaches. In the end, only actually dynamicmasks are generated. We have evaluated DOT with ORB-SLAM 2 [1] in three public datasets. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM2, especially in highly dynamic scenes.

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