LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
This paper presents LAPS, a replay management framework that combines reliability-based active pooling and uncertainty-guided active sampling to address catastrophic forgetting in incremental neural distance field mapping, improving reconstruction completeness while maintaining geometric accuracy.
[2605.15496] LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
[Submitted on 15 May 2026]
Title:LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
View a PDF of the paper titled LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields, by Dongjae Lee and 4 other authors
View PDF HTML (experimental)
Abstract:Neural distance fields offer a compact and continuous representation of 3D geometry, making them attractive for incremental LiDAR mapping. However, their online optimization is vulnerable to catastrophic forgetting, where new observations can degrade previously reconstructed geometry. Replay-based training is commonly used to address this issue, but existing methods typically rely on passive replay buffers and uniform sampling, which can waste memory on redundant observations and under-train poorly constrained regions. We propose LAPS, a replay management framework for incremental neural mapping that improves both replay retention and replay allocation during online updates. LAPS combines reliability-based active pooling to retain reliable historical samples under limited memory with uncertainty-guided active sampling to focus optimization on under-constrained regions. Experiments on synthetic and real-world benchmarks show that LAPS consistently improves reconstruction completeness while maintaining competitive geometric accuracy. On Oxford Spires, it improves recall by 4.66 pp and F1-score by 3.79 pp over PIN-SLAM on the Blenheim Palace 05 sequence. We release our open source implementation at: this https URL.
Comments: accepted at RA-L 2026
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.15496 [cs.RO]
(or arXiv:2605.15496v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.15496
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dongjae Lee [view email] [v1] Fri, 15 May 2026 00:22:31 UTC (8,695 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields, by Dongjae Lee and 4 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-05
Change to browse by:
cs cs.CV
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
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.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)