arXiv subjects

Tobias Fischer

Publications and source records attributed to Tobias Fischer.

At least 55 records · Page 3Linked to original sources

arXiv2025

Occurrence of fast neutrino flavor conversions in QCD phase-transition supernovae

Core-collapse supernovae undergoing a first-order quantum chromodynamics (QCD) phase transition experience the collapse of the central proto-neutron star that leads to a second bounce. This event is accompanied by the release of a second neutrino burst. Unlike the first stellar core bounce neutrino burst which consists exclusively of electron neutrinos, the second burst is dominated by electron antineutrinos. Such a condition makes QCD supernovae an ideal site for the occurrence of fast neutrino flavor conversion (FFC), which can lead to rapid flavor equilibration and significantly impact the related neutrino signal. In this work, we perform a detailed analysis of the conditions for fast flavor instability (FFI) around and after the second neutrino burst in QCD phase transition supernova models launched from 25~$M_\odot$ and 40~$M_\odot$ progenitor models. We evaluate the relevant instability criteria and find two major phases of FFC. The first phase is closely associated with the collapse and the rapidly expanding shock wave, which is a direct consequence of the proto-neutron star collapse due to the phase transition. The second phase takes place a few milliseconds later when electron degeneracy is restored near the proto-neutron star surface. We also characterize the growth rate of FFI and estimate its impact on the evolution of the neutrino flavor content. The potential observational consequences on neutrino signals are evaluated by comparing a scenario assuming complete flavor equipartition with other scenarios without FFC. Finally, we investigate how FFC may influences $r$-process nucleosynthesis associated with QCD phase transition driven supernova explosions.

astro-ph.HE↗

arXiv2025

Prepare for Warp Speed: Sub-millisecond Visual Place Recognition Using Event Cameras

Visual Place Recognition (VPR) enables systems to identify previously visited locations within a map, a fundamental task for autonomous navigation. Prior works have developed VPR solutions using event cameras, which asynchronously measure per-pixel brightness changes with microsecond temporal resolution. However, these approaches rely on dense representations of the inherently sparse camera output and require tens to hundreds of milliseconds of event data to predict a place. Here, we break this paradigm with Flash, a lightweight VPR system that predicts places using sub-millisecond slices of event data. Our method is based on the observation that active pixel locations provide strong discriminative features for VPR. Flash encodes these active pixel locations using efficient binary frames and computes similarities via fast bitwise operations, which are then normalized based on the relative event activity in the query and reference frames. Flash improves Recall@1 for sub-millisecond VPR over existing baselines by 11.33x on the indoor QCR-Event-Dataset and 5.92x on the 8 km Brisbane-Event-VPR dataset. Moreover, our approach reduces the duration for which the robot must operate without awareness of its position, as evidenced by a localization latency metric we term Time to Correct Match (TCM). To the best of our knowledge, this is the first work to demonstrate sub-millisecond VPR using event cameras.

cs.RO↗

arXiv2025

RMMI: Reactive Mobile Manipulation using an Implicit Neural Map

Mobile manipulator robots operating in complex domestic and industrial environments must effectively coordinate their base and arm motions while avoiding obstacles. While current reactive control methods gracefully achieve this coordination, they rely on simplified and idealised geometric representations of the environment to avoid collisions. This limits their performance in cluttered environments. To address this problem, we introduce RMMI, a reactive control framework that leverages the ability of neural Signed Distance Fields (SDFs) to provide a continuous and differentiable representation of the environment's geometry. RMMI formulates a quadratic program that optimises jointly for robot base and arm motion, maximises the manipulability, and avoids collisions through a set of inequality constraints. These constraints are constructed by querying the SDF for the distance and direction to the closest obstacle for a large number of sampling points on the robot. We evaluate RMMI both in simulation and in a set of real-world experiments. For reaching in cluttered environments, we observe a 25% increase in success rate. For additional details, code, and experiment videos, please visit https://rmmi.github.io/.

cs.RO↗

arXiv2025

FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization

Hierarchical visual localization methods achieve state-of-the-art accuracy but require substantial memory as they need to store all database images. Direct 2D-3D matching requires significantly less memory but suffers from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator. This operator rearranges the local descriptor space so that geographically nearby local descriptors are closer in the feature space according to the global descriptors. This decreases the number of irrelevant competing descriptors, especially if they are geographically distant, thus increasing the correct matching likelihood. We consistently improve the accuracy over local-only systems, and we achieve performance close to hierarchical methods while using 43\% less memory and running 1.6 times faster. Extensive experiments on four challenging datasets -- Cambridge Landmarks, Aachen Day/Night, RobotCar Seasons, and Extended CMU Seasons -- demonstrate that, for the first time, direct matching algorithms can benefit from global descriptors without compromising computational efficiency. Our code is available at \href{https://github.com/sontung/descriptor-disambiguation}{https://github.com/sontung/descriptor-disambiguation}.

cs.CV↗

arXiv2025

FlowR: Flowing from Sparse to Dense 3D Reconstructions

3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense captures are needed to match the high-quality expectations of applications like Virtual Reality (VR). However, such dense captures are very laborious and expensive to obtain. Existing works have explored using 2D generative models to alleviate this requirement by distillation or generating additional training views. These models typically rely on a noise-to-data generative process conditioned only on a handful of reference input views, leading to hallucinations, inconsistent generation results, and subsequent reconstruction artifacts. Instead, we propose a multi-view, flow matching model that learns a flow to directly connect novel view renderings from possibly sparse reconstructions to renderings that we expect from dense reconstructions. This enables augmenting scene captures with consistent, generated views to improve reconstruction quality. Our model is trained on a novel dataset of 3.6M image pairs and can process up to 45 views at 540x960 resolution (91K tokens) on one H100 GPU in a single forward pass. Our pipeline consistently improves NVS in sparse- and dense-view scenarios, leading to higher-quality reconstructions than prior works across multiple, widely-used NVS benchmarks.

cs.CV↗

arXiv2025

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. Our approach is agnostic to the underlying VPR technique and effectively 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, and present ablation studies including an analysis of the interactions between our SMR predictor and the selected sequence length.

cs.CV↗

arXiv2025

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.

cs.CV↗

arXiv2025

A compact neuromorphic system for ultra-energy-efficient, on-device robot localization

Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms which still require accurate long-endurance localization. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of bio-realistic networks. In order to overcome this challenge, fusion of hardware and algorithms is critical to employ this specialized computing paradigm. Here, we demonstrate a neuromorphic localization system that performs competitive place recognition in up to 8 kilometers of traversal using models as small as 180 kilobytes with 44,000 parameters, while consuming less than 8% of the energy required by conventional methods. Our Locational Encoding with Neuromorphic Systems (LENS) integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SynSense Speck chip, enabling real-time, energy-efficient localization on a hexapod robot. When compared to a benchmark place recognition method, Sum-of-Absolute-Differences (SAD), LENS performs comparably in overall precision. LENS represents an accurate fully neuromorphic localization system capable of large-scale, on-device deployment for energy efficient robotic place recognition. Neuromorphic computing enables resource-constrained robots to perform energy efficient, accurate localization.

cs.RO↗

arXiv2025

Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics

We present Smac Planner, an openly available, search-based planning framework that addresses the critical need for kinematically feasible path planning across diverse robot platforms. Smac Planner provides high-performance implementations of Cost-Aware A*, Hybrid-A*, and State Lattice planners that can be deployed for Ackermann, legged, and other large non-circular robots. Our framework introduces novel "Cost-Aware" variations that significantly improve performance in complex environments common to mobile robotics while maintaining kinematic feasibility constraints. Integrated as the standard planning system within the popular ROS 2 Navigation stack, Nav2, Smac Planner now powers thousands of robots worldwide across academic research, commercial applications, and field deployments.

cs.RO↗

arXiv2025

Fast neutrino flavor conversions in a supernova: Emergence, evolution, and effects

Fast flavor conversions (FFCs) of neutrinos, which can occur in core-collapse supernovae (CCSNe), are multiangle effects. They depend on the angular distribution of the neutrino's electron lepton number (ELN). In this work, we present a comprehensive study of the FFCs by solving the multienergy and multiangle quantum kinetic equations with an extended set of collisional weak processes based on a static and spherically symmetric CCSN matter background profile. We investigate the emergence and evolution of FFCs in models featuring different ELN angular distributions, considering scenarios with two and three neutrino flavors. The spectrogram method is utilized to illustrate the small-scale spatial structure, and we show that this structure of neutrino flavor coherence and number densities in the nonlinear regime is qualitatively consistent with the dispersion relation analysis. On the coarse-grained level, we find that different asymptotic states can be achieved following the FFCs depending on the locations and shapes of the ELN distributions, despite sharing a common feature of the elimination of the ELN angular crossing. While equilibration among different neutrino flavors may be achieved immediately after the prompt FFCs, it is not a general outcome of the asymptotic state, as subsequent feedback effects from collisional neutrino-matter interactions come into play, particularly for cases where FFCs occur inside the neutrinosphere. The impacts of FFCs and the feedback effect on the net neutrino heating rates, the equilibrium electron fraction of CCSN matter, and the free-streaming neutrino energy spectra are quantitatively assessed. Other aspects including the impact of the vacuum term and the coexistence with other type of flavor instabilities are also discussed.

astro-ph.HE↗

arXiv2025

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

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 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.

cs.CV↗

arXiv2025

Applications of Spiking Neural Networks in Visual Place Recognition

In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for their largely-unrealized potential energy efficiency and low latency particularly when implemented on neuromorphic hardware. Our paper highlights three advancements for SNNs in Visual Place Recognition (VPR). Firstly, we propose Modular SNNs, where each SNN represents a set of non-overlapping geographically distinct places, enabling scalable networks for large environments. Secondly, we present Ensembles of Modular SNNs, where multiple networks represent the same place, significantly enhancing accuracy compared to single-network models. Each of our Modular SNN modules is compact, comprising only 1500 neurons and 474k synapses, making them ideally suited for ensembling due to their small size. Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition. We demonstrate competitive performance of our method on a range of datasets, including higher responsiveness to ensembling compared to conventional VPR techniques and higher R@1 improvements with sequence matching than VPR techniques with comparable baseline performance. Our contributions highlight the viability of SNNs for VPR, offering scalable and robust solutions, and paving the way for their application in various energy-sensitive robotic tasks.

cs.CV↗

arXiv2025

Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation

Efficient spatial navigation is a hallmark of the mammalian brain, inspiring the development of neuromorphic systems that mimic biological principles. Despite progress, implementing key operations like back-tracing and handling ambiguity in bio-inspired spiking neural networks remains an open challenge. This work proposes a mechanism for activity back-tracing in arbitrary, uni-directional spiking neuron graphs. We extend the existing replay mechanism of the spiking hierarchical temporal memory (S-HTM) by our spike timing-dependent threshold adaptation (STDTA), which enables us to perform path planning in networks of spiking neurons. We further present an ambiguity dependent threshold adaptation (ADTA) for identifying places in an environment with less ambiguity, enhancing the localization estimate of an agent. Combined, these methods enable efficient identification of the shortest path to an unambiguous target. Our experiments show that a network trained on sequences reliably computes shortest paths with fewer replays than the steps required to reach the target. We further show that we can identify places with reduced ambiguity in multiple, similar environments. These contributions advance the practical application of biologically inspired sequential learning algorithms like the S-HTM towards neuromorphic localization and navigation.

cs.NE↗

arXiv2025

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, a conda-based packaging system for ROS, 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. ROS2WASM significantly enhances the 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 https://www.ros2wasm.dev, a web platform enabling 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.

cs.RO↗

arXiv2025

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

cs.AI↗

arXiv2025

Hyperons during proto-neutron star deleptonization and the emission of dark flavoured particles

Complementary to high-energy experimental efforts, indirect astrophysical searches of particles beyond the standard model have long been pursued. The present article follows the latter approach and considers, for the first time, the self-consistent treatment of the energy losses from dark flavoured particles produced in the decay of hyperons during a core-collapse supernova (CCSN). To this end, general relativistic supernova simulations in spherical symmetry are performed, featuring six-species Boltzmann neutrino transport, and covering the long-term evolution of the nascent remnant proto-neutron star (PNS) deleptonization for several tens of seconds. A well-calibrated hyperon equation of state (EOS) is therefore implemented into the supernova simulations and tested against the corresponding nucleonic model. It is found that supernova observables, such as the neutrino signal, are robustly insensitive to the appearance of hyperons for the simulation times considered in the present study. The presence of hyperons enables an additional channel for the appearance of dark sector particles, which is considered at the level of the $Λ$ hyperon decay. Assuming massless particles that escape the PNS after being produced, these channels expedite the deleptonizing PNS and the cooling behaviour. This, in turn, shortens the neutrino emission timescale. The present study confirms the previously estimated upper limits on the corresponding branching ratios for low and high mass PNS, by effectively reducing the neutrino emission timescale by a factor of two. This is consistent with the classical argument deduced from the neutrino detection associated with SN1987A.

astro-ph.HE↗

arXiv2024

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.

cs.CV↗

arXiv2024

Dynamic 3D Gaussian Fields for Urban Areas

We present an efficient neural 3D scene representation for novel-view synthesis (NVS) in large-scale, dynamic urban areas. Existing works are not well suited for applications like mixed-reality or closed-loop simulation due to their limited visual quality and non-interactive rendering speeds. Recently, rasterization-based approaches have achieved high-quality NVS at impressive speeds. However, these methods are limited to small-scale, homogeneous data, i.e. they cannot handle severe appearance and geometry variations due to weather, season, and lighting and do not scale to larger, dynamic areas with thousands of images. We propose 4DGF, a neural scene representation that scales to large-scale dynamic urban areas, handles heterogeneous input data, and substantially improves rendering speeds. We use 3D Gaussians as an efficient geometry scaffold while relying on neural fields as a compact and flexible appearance model. We integrate scene dynamics via a scene graph at global scale while modeling articulated motions on a local level via deformations. This decomposed approach enables flexible scene composition suitable for real-world applications. In experiments, we surpass the state-of-the-art by over 3 dB in PSNR and more than 200 times in rendering speed.

cs.CV↗