aiqbench analysis
This research tackles the computational bottleneck of training single global models for large-scale mapping by decomposing the task into local submaps optimized through variance-weighted fusion. By leveraging Gaussian-conjugate inference and uncertainty-aware pose optimization, the method significantly reduces storage requirements and joining time while maintaining high accuracy on LiDAR data. The approach represents a meaningful advancement in scalable spatial representation learning for autonomous systems.
Large-Scale Continuous Occupancy Mapping via Variance-Weighted Submap Joining
arXiv:2606.10442v2 Announce Type: replace Abstract: Continuous occupancy mapping methods that learn spatial representations from sensor observations support predictions at arbitrary locations, but training a single global model can become computationally expensive as the amount of training data grows during large-scale mapping. Constructing local continuous occupancy submaps reduces the size of individual learning problems, but relative pose errors can cause inconsistent predictions in overlapping regions. We address these challenges by formulating large-scale continuous occupancy mapping as a submap joining problem. Each local submap is represented by a relevance vector machine regressing a continuous average log-odds field. Repeated observations within each cell are summarized by their average log-odds and observation count, yielding a Gaussian likelihood with count-dependent precision and enabling Gaussian-conjugate inference. The resulting local fields provide predictive means, variances, and analytical spatial derivatives. We then formulate submap joining as a joint pose--field optimization problem, with consistency constraints weighted by the predictive uncertainty of overlapping local submaps. With uncertainty weights held fixed within each Gauss--Newton iteration, our joint pose--field system can be reduced to an equivalent pose-only system. After the optimized submap poses are obtained, the global field value at any covered location can be recovered in closed form through variance-weighted fusion of predictions from the corresponding local submaps, without training an additional global model. Experiments on simulated and real-world 2D LiDAR datasets demonstrate improved pose and occupancy-map accuracy over the evaluated baselines, reduced map storage and joining time, and improved map-construction scalability.
Abstract:Continuous occupancy mapping methods that learn spatial representations from sensor observations support predictions at arbitrary locations, but training a single global model can become computationally expensive as the amount of training data grows during large-scale mapping. Constructing local continuous occupancy submaps reduces the size of individual learning problems, but relative pose errors can cause inconsistent predictions in overlapping regions. We address these challenges by formulating large-scale continuous occupancy mapping as a submap joining problem. Each local submap is represented by a relevance vector machine regressing a continuous average log-odds field. Repeated observations within each cell are summarized by their average log-odds and observation count, yielding a Gaussian likelihood with count-dependent precision and enabling Gaussian-conjugate inference. The resulting local fields provide predictive means, variances, and analytical spatial derivatives. We then formulate submap joining as a joint pose--field optimization problem, with consistency constraints weighted by the predictive uncertainty of overlapping local submaps. With uncertainty weights held fixed within each Gauss--Newton iteration, our joint pose--field system can be reduced to an equivalent pose-only system. After the optimized submap poses are obtained, the global field value at any covered location can be recovered in closed form through variance-weighted fusion of predictions from the corresponding local submaps, without training an additional global model. Experiments on simulated and real-world 2D LiDAR datasets demonstrate improved pose and occupancy-map accuracy over the evaluated baselines, reduced map storage and joining time, and improved map-construction scalability.
Robotics (cs.RO)
arXiv:2606.10442 [cs.RO]
(or arXiv:2606.10442v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.10442 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 327 words
Related coverage
Information-Preserving Continuous Occupancy Mapping with Variance-Weighted Submap Joining
arXiv · Jun 10, 2026
Performance Profiling on AMD GPUs – Part 5: Profiling-Driven Kernel Optimization with an AI Code-Assist Tool
AMD · Jul 16, 2026
J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses
LessWrong · Oct 8, 2026
[AINews] not much happened today
Latent Space · Sep 10, 2026