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arxiv: 2604.12436 · v1 · submitted 2026-04-14 · 💻 cs.RO

D-BDM: A Direct and Efficient Boundary-Based Occupancy Grid Mapping Framework for LiDARs

Benxu Tang , Yixi Cai , Fanze Kong , Longji Yin , Fu Zhang This is my paper

Pith reviewed 2026-05-10 15:34 UTC · model grok-4.3

classification 💻 cs.RO
keywords occupancy grid mappingLiDARboundary-basedray casting3D mappingmemory efficiencyupdate latencyautonomous robots
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The pith

The D-BDM framework achieves lower update times and memory use for LiDAR-based 3D occupancy mapping by truncating ray casting to boundary exteriors and using direct updates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a new framework called D-BDM for building 3D occupancy grids from LiDAR data. Traditional methods store all voxels in space, using lots of memory, and cast rays through the entire volume, which takes time. Prior boundary-based methods save memory by keeping only surface voxels but still do full ray casting. D-BDM restricts ray casting to just outside the boundary and updates the boundary directly without extra local grids. This matters because it could let robots map large unknown areas in real time with less computer power.

Core claim

The authors introduce D-BDM, which uses a truncated ray casting strategy that restricts voxel traversal to the exterior of the boundary, dramatically reducing the number of updated voxels, and a direct boundary update mechanism that eliminates the need for an auxiliary local 3D occupancy grid, simplifying the pipeline and cutting memory use. Evaluations show significantly lower update time and memory consumption than baselines and prior boundary approaches.

What carries the argument

truncated ray casting strategy restricted to the boundary exterior combined with direct boundary update mechanism

Load-bearing premise

Restricting ray casting to the exterior of the boundary and using direct updates preserves map accuracy and completeness equivalent to full ray casting methods without introducing artifacts or missing occupied regions.

What would settle it

Running D-BDM and a full ray casting method on identical LiDAR data from public datasets and verifying if the resulting occupancy maps show the same occupied and free spaces without discrepancies.

Figures

Figures reproduced from arXiv: 2604.12436 by Benxu Tang, Fanze Kong, Fu Zhang, Longji Yin, Yixi Cai.

Figure 1
Figure 1. Figure 1: (a) Illustration of the proposed update scheme. Ray traversal is [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) An uniform occupancy grid with free, unknown, and occupied regions, represented by green, blue, and grey, respectively. (b) The boundary [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The motivation for applying the slab method after the initial [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) The boundary map before the update, where voxels with free, unknown and occupied state are colored as green, blue and grey, respectively. (b) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) An illustration of the long-range navigation task in a multi-level [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

Efficient and scalable 3D occupancy mapping is essential for autonomous robot applications in unknown environments. However, traditional occupancy grid representations suffer from two fundamental limitations. First, explicitly storing all voxels in three-dimensional space leads to prohibitive memory consumption. Second, exhaustive ray casting incurs high update latency. A recent representation alleviate memory demands by maintaining only the voxels on the two-dimensional boundary, yet they still rely on full ray casting updates. This work advances the boundary-based framework with a highly efficient update scheme. We introduce a truncated ray casting strategy that restricts voxel traversal to the exterior of the boundary, which dramatically reduces the number of updated voxels. In addition, we propose a direct boundary update mechanism that removes the need for an auxiliary local 3D occupancy grid, further reducing memory usage and simplifying the map update pipeline. We name our framework as D-BDM. Extensive evaluations on public datasets demonstrate that our approach achieves significantly lower update time and reduced memory consumption compared with the baseline methods, as well as the prior boundary-based approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces D-BDM, a boundary-based 3D occupancy grid mapping framework for LiDARs. It advances prior boundary representations by restricting ray casting to the exterior of the maintained 2D boundary (truncated ray casting) and applying updates directly to the boundary without an auxiliary local 3D grid. The central claims are substantially lower update latency and memory consumption relative to standard occupancy grids and the prior boundary-based method, with map accuracy and completeness preserved, supported by evaluations on public datasets.

Significance. If the truncated exterior ray casting and direct boundary updates indeed preserve completeness and accuracy equivalent to full ray casting (i.e., correctly labeling all free space up to the first hit and handling boundary insertions/deletions without artifacts), the work would offer a meaningful efficiency gain for real-time robotic mapping in large-scale or resource-limited settings.

major comments (2)
  1. [Abstract] Abstract: The manuscript asserts that the proposed restrictions preserve map accuracy and completeness, yet reports only timing and memory gains; no quantitative accuracy metrics (voxel-wise IoU, precision-recall on occupied cells, or boundary Hausdorff distance) versus full ray-casting baselines are provided. This leaves the load-bearing equivalence assumption unverified.
  2. [Evaluation] Evaluation section: The experimental results lack reported error bars, exact baseline implementations, dataset splits, and any ablation on concave geometries or dynamic boundary changes, which are required to substantiate that interior free-space labeling and newly occupied voxels remain correctly updated after truncation and direct boundary edits.
minor comments (2)
  1. The description of the direct boundary update mechanism would benefit from a clearer algorithmic pseudocode or step-by-step enumeration of how occupancy changes propagate without the auxiliary grid.
  2. Figure captions and axis labels in the timing/memory plots should explicitly state the units and the precise set of compared methods for immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects for strengthening the validation of our efficiency claims while ensuring map fidelity. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The manuscript asserts that the proposed restrictions preserve map accuracy and completeness, yet reports only timing and memory gains; no quantitative accuracy metrics (voxel-wise IoU, precision-recall on occupied cells, or boundary Hausdorff distance) versus full ray-casting baselines are provided. This leaves the load-bearing equivalence assumption unverified.

    Authors: We agree that quantitative verification is necessary to substantiate the preservation of accuracy and completeness. In the revised manuscript, we will add voxel-wise IoU, precision-recall on occupied cells, and boundary Hausdorff distance metrics computed against full ray-casting baselines using the same public datasets. These results will be presented in the Evaluation section to directly address the equivalence assumption. revision: yes

  2. Referee: [Evaluation] Evaluation section: The experimental results lack reported error bars, exact baseline implementations, dataset splits, and any ablation on concave geometries or dynamic boundary changes, which are required to substantiate that interior free-space labeling and newly occupied voxels remain correctly updated after truncation and direct boundary edits.

    Authors: We acknowledge the need for greater experimental rigor and reproducibility. The revised manuscript will include error bars on all timing and memory results, explicit details on baseline implementations and dataset splits, and new ablation studies on concave geometries as well as dynamic boundary insertion/deletion scenarios. These additions will demonstrate correct interior free-space labeling and update behavior under the truncated ray casting and direct boundary mechanisms. revision: yes

Circularity Check

0 steps flagged

No significant circularity; algorithmic efficiency claims rest on external dataset evaluations

full rationale

The paper introduces truncated exterior ray casting and direct boundary updates as explicit algorithmic modifications to an existing boundary-based occupancy mapping framework. These modifications are described directly in the method section without any equations that define a quantity in terms of itself or rename fitted parameters as predictions. Performance claims (lower update time and memory) are justified solely by timing and memory measurements on public datasets, which constitute independent external benchmarks rather than self-referential fits. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work appear in the provided text; the derivation chain is therefore a standard sequence of design choices followed by empirical verification and does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract; the work applies standard occupancy grid and ray casting concepts with new algorithmic restrictions.

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Darpa subterranean challenge: Multi-robotic exploration of underground environments,

    T. Rou ˇcek, M. Pecka, P. ˇC´ıˇzek, T. Pet ˇr´ıˇcek, J. Bayer, V . ˇSalansk`y, D. He ˇrt, M. Petrl ´ık, T. B ´aˇca, V . Spurn `y,et al., “Darpa subterranean challenge: Multi-robotic exploration of underground environments,” in Modelling and Simulation for Autonomous Systems: 6th International Conference, MESAS 2019, Palermo, Italy, October 29–31, 2019, Re...

    work page 2019

  2. [2]

    Cerberus in the darpa subterranean challenge,

    M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart,et al., “Cerberus in the darpa subterranean challenge,”Science Robotics, vol. 7, no. 66, p. eabp9742, 2022

    work page 2022

  3. [3]

    Emergency response by robots to fukushima-daiichi accident: summary and lessons learned,

    S. Kawatsuma, M. Fukushima, and T. Okada, “Emergency response by robots to fukushima-daiichi accident: summary and lessons learned,” Industrial Robot: An International Journal, vol. 39, no. 5, pp. 428–435, 2012

    work page 2012

  4. [4]

    A study on the disaster response scenarios using robot technology,

    O. SeungSub, H. Jehun, J. Hyunjung, L. Soyeon, and S. Jinho, “A study on the disaster response scenarios using robot technology,” in 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2017, pp. 520–523

    work page 2017

  5. [5]

    Autonomous exploration for infrastructure modeling with a micro aerial vehicle,

    L. Yoder and S. Scherer, “Autonomous exploration for infrastructure modeling with a micro aerial vehicle,” inField and Service Robotics: Results of the 10th International Conference. Springer, 2016, pp. 427– 440

    work page 2016

  6. [6]

    Autonomous cave surveying with an aerial robot,

    W. Tabib, K. Goel, J. Yao, C. Boirum, and N. Michael, “Autonomous cave surveying with an aerial robot,”IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1016–1032, 2021

    work page 2021

  7. [7]

    Avoiding dynamic small obstacles with onboard sensing and computation on aerial robots,

    F. Kong, W. Xu, Y . Cai, and F. Zhang, “Avoiding dynamic small obstacles with onboard sensing and computation on aerial robots,”IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7869–7876, 2021

    work page 2021

  8. [8]

    Safety-assured high-speed navigation for mavs,

    Y . Ren, F. Zhu, G. Lu, Y . Cai, L. Yin, F. Kong, J. Lin, N. Chen, and F. Zhang, “Safety-assured high-speed navigation for mavs,”Science Robotics, vol. 10, no. 98, p. eado6187, 2025

    work page 2025

  9. [9]

    Far planner: Fast, attemptable route planner using dynamic visibility update,

    F. Yang, C. Cao, H. Zhu, J. Oh, and J. Zhang, “Far planner: Fast, attemptable route planner using dynamic visibility update,” in2022 ieee/rsj international conference on intelligent robots and systems (iros). IEEE, 2022, pp. 9–16

    work page 2022

  10. [10]

    Graph-based path planning for autonomous robotic exploration in subterranean environments,

    T. Dang, F. Mascarich, S. Khattak, C. Papachristos, and K. Alexis, “Graph-based path planning for autonomous robotic exploration in subterranean environments,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019, pp. 3105–3112

    work page 2019

  11. [11]

    Fuel: Fast uav exploration using incremental frontier structure and hierarchical planning,

    B. Zhou, Y . Zhang, X. Chen, and S. Shen, “Fuel: Fast uav exploration using incremental frontier structure and hierarchical planning,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 779–786, 2021

    work page 2021

  12. [12]

    Bubble explorer: Fast uav exploration in large-scale and cluttered 3d- environments using occlusion-free spheres,

    B. Tang, Y . Ren, F. Zhu, R. He, S. Liang, F. Kong, and F. Zhang, “Bubble explorer: Fast uav exploration in large-scale and cluttered 3d- environments using occlusion-free spheres,” in2023 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 1118–1125

    work page 2023

  13. [13]

    Robot evidence grids,

    M. C. M. H. P. Moravec, “Robot evidence grids,”CMU Robotics Institute Technical Report CMU-RI-TR-96-06, 1996

    work page 1996

  14. [14]

    Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,

    Y . Ren, Y . Cai, F. Zhu, S. Liang, and F. Zhang, “Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,”arXiv preprint arXiv:2302.14819, 2023

    work page arXiv 2023

  15. [15]

    Real-time 3d reconstruction at scale using voxel hashing,

    M. Nießner, M. Zollh ¨ofer, S. Izadi, and M. Stamminger, “Real-time 3d reconstruction at scale using voxel hashing,”ACM Transactions on Graphics (ToG), vol. 32, no. 6, pp. 1–11, 2013

    work page 2013

  16. [16]

    Octomap: An efficient probabilistic 3d mapping framework based on octrees,

    A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3d mapping framework based on octrees,”Autonomous robots, vol. 34, pp. 189–206, 2013

    work page 2013

  17. [17]

    Ufomap: An efficient probabilistic 3d mapping framework that embraces the unknown,

    D. Duberg and P. Jensfelt, “Ufomap: An efficient probabilistic 3d mapping framework that embraces the unknown,”IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6411–6418, 2020

    work page 2020

  18. [18]

    Occupancy grid mapping without ray-casting for high-resolution lidar sensors,

    Y . Cai, F. Kong, Y . Ren, F. Zhu, J. Lin, and F. Zhang, “Occupancy grid mapping without ray-casting for high-resolution lidar sensors,”IEEE Transactions on Robotics, 2023

    work page 2023

  19. [19]

    Memory-efficient boundary map for large-scale occupancy grid mapping,

    B. Tang, Y . Ren, Y . Cai, F. Kong, W. Liu, F. Zhu, L. Yin, L. Shi, and F. Zhang, “Memory-efficient boundary map for large-scale occupancy grid mapping,”The International Journal of Robotics Research, vol. 0, no. 0, p. 02783649261425266, 0. [Online]. Available: https://doi.org/10.1177/02783649261425266

    work page doi:10.1177/02783649261425266

  20. [20]

    Building an environment model using depth information,

    Y . Roth-Tabak and R. Jain, “Building an environment model using depth information,”Computer, vol. 22, no. 6, pp. 85–90, 1989

    work page 1989

  21. [21]

    Robot navigation: Integrating perception, environmental constraints and task execution within a probabilistic framework,

    A. Elfes, “Robot navigation: Integrating perception, environmental constraints and task execution within a probabilistic framework,” in International Workshop on Reasoning with uncertainty in Robotics. Springer, 1995, pp. 91–130

    work page 1995

  22. [22]

    Hierarchies of octrees for efficient 3d mapping,

    K. M. Wurm, D. Hennes, D. Holz, R. B. Rusu, C. Stachniss, K. Kono- lige, and W. Burgard, “Hierarchies of octrees for efficient 3d mapping,” in2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2011, pp. 4249–4255

    work page 2011

  23. [23]

    A ray-box intersection algorithm and efficient dynamic voxel rendering,

    A. Majercik, C. Crassin, P. Shirley, and M. McGuire, “A ray-box intersection algorithm and efficient dynamic voxel rendering,”Journal of Computer Graphics Techniques (JCGT), vol. 7, no. 3, pp. 66–81, September 2018. [Online]. Available: http://jcgt.org/published/0007/03/ 04/

    work page 2018

  24. [24]

    HeLiPR: Heterogeneous LiDAR Dataset for Inter-LiDAR Place Recognition Under Spatiotemporal Variations.The In- ternational Journal of Robotics Research, 43(3):1867– 1883, 2024

    M. Jung, W. Yang, D. Lee, H. Gil, G. Kim, and A. Kim, “Helipr: Heterogeneous lidar dataset for inter-lidar place recognition under spatiotemporal variations,”The International Journal of Robotics Research, vol. 43, no. 12, pp. 1867–1883, 2024. [Online]. Available: https://doi.org/10.1177/02783649241242136

    work page doi:10.1177/02783649241242136 2024

  25. [25]

    Vision meets robotics: The kitti dataset,

    A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,”The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013

    work page 2013

  26. [26]

    The newer college dataset: Handheld lidar, inertial and vision with ground truth,

    M. Ramezani, Y . Wang, M. Camurri, D. Wisth, M. Mattamala, and M. Fallon, “The newer college dataset: Handheld lidar, inertial and vision with ground truth,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 4353–4360

    work page 2020