pith. sign in

arxiv: 2605.23350 · v1 · pith:RADWPLSBnew · submitted 2026-05-22 · 💻 cs.RO

Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors

Zhiwen Zhu , Jiaqi Chen , Xiangyi Huang , Meiqi Hu , Boyu Zhou This is my paper

Pith reviewed 2026-05-25 04:22 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-floor explorationreachable graphground robotsstructural priorshierarchical plannerfrontier detectionautonomous navigation
0
0 comments X

The pith

Ground robots explore multi-floor buildings by maintaining an incremental reachable graph with projected structural priors from known floors.

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

The paper develops a framework for autonomous multi-floor exploration by ground robots that overcomes the inability of standard 2D and 2.5D maps to represent stairs, ramps, and overlapping elevations. It builds a sparse incremental reachable graph over support surfaces that keeps tentative graph elements to preserve possible connectivity even with limited observations. Task-zone priors are projected from an explored floor to seed a hypothetical graph on an unexplored floor, then reconciled step by step as new sensor data arrives. A hierarchical planner reasons jointly over the confirmed and hypothetical parts of the graph to select frontiers and paths. Simulation results indicate higher exploration speed and more complete maps than baselines, while real-robot tests show the system runs in real time onboard.

Core claim

The central claim is that an incremental reachable graph, constructed sparsely over reachable support surfaces and augmented by projected task-zone priors that initialize hypothetical structures on new floors, enables stable detection of physically reachable frontiers and global guidance across multiple floors without dense volumetric maps.

What carries the argument

Incremental reachable graph: a sparse graph over reachable support surfaces that retains tentative elements under sparse observations to maintain potential connectivity and support frontier detection.

If this is right

  • Exploration proceeds beyond the currently mapped floor by using hypothetical graph elements for planning.
  • Frontier detection remains stable because tentative connections are preserved until observations confirm or refute them.
  • The hierarchical planner produces global guidance by jointly considering confirmed and hypothetical structures.
  • Simulation trials show higher efficiency and mapping completeness than the evaluated baseline methods.
  • Onboard real-world runs achieve real-time performance and practical feasibility in multi-floor settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The sparse graph approach may reduce memory and computation demands compared with full 3D reconstructions in vertically connected spaces.
  • The projection and reconciliation steps could generalize to other environments that contain ramps or elevators once appropriate priors are defined.
  • Integration with dynamic obstacle avoidance would test whether the tentative elements remain useful when the environment changes rapidly.

Load-bearing premise

Task-zone priors projected from an explored floor can initialize a hypothetical graph on the target floor and be reconciled incrementally with observations without creating unreachable or invalid connections.

What would settle it

A test case in which projected priors produce persistent invalid connectivity that incremental updates never resolve, causing the planner to select unreachable frontiers or fail to advance to new floors.

Figures

Figures reproduced from arXiv: 2605.23350 by Boyu Zhou, Jiaqi Chen, Meiqi Hu, Xiangyi Huang, Zhiwen Zhu.

Figure 1
Figure 1. Figure 1: Real-world multi-floor exploration in a campus building. The central image depicts an exploration snapshot on the second floor, visualizing the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System architecture of the proposed multi-floor exploration framework. Module 1 maintains an incremental reachable graph for exploration by [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A 2D example illustrating how tentative nodes preserve potential [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Incremental maintenance of task-zone partitions on the sparse graph. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Resulting maps from our method and trajectories of all methods [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Real-world experiments in (a) a large courtyard building scene and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping traversable surfaces such as stairs, ramps, and multiple reachable elevations. This letter presents a multi-floor exploration framework based on an incremental reachable graph. Built as a sparse graph over reachable support surfaces, the graph preserves potentially valid connectivity through tentative graph elements under sparse observations and enables stable, physically reachable frontier detection. To guide exploration beyond the currently mapped floor, we project task-zone priors from an explored floor to initialize a hypothetical graph on the target floor and reconcile it incrementally with incoming observations. A hierarchical planner then jointly reasons over confirmed and hypothetical structures for global guidance. In simulation, the proposed method demonstrates improved exploration efficiency and mapping completeness compared to evaluated baselines. Furthermore, onboard real-world experiments validate its practical feasibility and real-time performance.

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

0 major / 1 minor

Summary. The paper presents a multi-floor exploration framework for ground robots based on an incremental reachable graph over reachable support surfaces. The graph uses tentative elements to preserve connectivity under sparse observations and enables stable frontier detection. Task-zone priors are projected from explored floors to initialize hypothetical graphs on target floors, which are reconciled incrementally with new observations. A hierarchical planner reasons jointly over confirmed and hypothetical structures. In simulation, it shows improved exploration efficiency and mapping completeness over baselines, and real-world onboard experiments validate feasibility and real-time performance.

Significance. If the claims hold, this work addresses an important challenge in robotics by enabling efficient multi-floor exploration where standard 2D and 2.5D maps are insufficient. The incremental reachable graph with structural priors and the hierarchical planner represent a promising approach for handling vertical connectivity in buildings. The inclusion of both simulation comparisons and real-world validation is a strength, providing evidence of practical applicability.

minor comments (1)
  1. [Abstract] Abstract: Consider specifying the number of simulation trials or the exact metrics used for 'improved exploration efficiency' to strengthen the claim.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our work on multi-floor exploration via an incremental reachable graph and structural priors, as well as the recommendation for minor revision. We appreciate the recognition of the approach's potential for handling vertical connectivity in buildings and the value placed on both simulation and real-world validation.

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The provided abstract and context describe a method using an incremental reachable graph, projection of task-zone priors, incremental reconciliation, and a hierarchical planner. No equations, fitted parameters, self-citations, or derivation steps are present that reduce a claimed prediction or result to its own inputs by construction. The central claims rest on the described algorithmic mechanisms and external experimental validation rather than self-referential definitions or renamings. No load-bearing step matches any enumerated circularity pattern.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only view yields no explicit free parameters, axioms, or invented entities beyond the core graph structure itself; full paper would be needed to audit these.

pith-pipeline@v0.9.0 · 5687 in / 961 out tokens · 31850 ms · 2026-05-25T04:22:22.219716+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Using occupancy grids for mobile robot perception and navigation,

    A. Elfes, “Using occupancy grids for mobile robot perception and navigation,”Computer, vol. 22, no. 6, pp. 46–57, 1989

    work page 1989

  2. [2]

    Universal trajectory optimization framework for differential drive robot class,

    M. Zhang, N. Chen, H. Wang, J. Qiu, Z. Han, Q. Ren, C. Xu, F. Gao, and Y . Cao, “Universal trajectory optimization framework for differential drive robot class,”IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 13 030–13 045, 2025

    work page 2025

  3. [3]

    Elevation mapping for locomotion and navigation using gpu,

    T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “Elevation mapping for locomotion and navigation using gpu,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 2273–2280

    work page 2022

  4. [4]

    Trip: Terrain traversability mapping with risk-aware prediction for enhanced online quadrupedal robot navigation,

    M. Oh, B. Yu, I. M. A. Nahrendra, S. Jang, H. Lee, D. Lee, S. Lee, Y . Kim, M. K. Christiansen, H. Lim, and H. Myung, “Trip: Terrain traversability mapping with risk-aware prediction for enhanced online quadrupedal robot navigation,”arXiv preprint arXiv:2411.17134, 2024

    work page arXiv 2024

  5. [5]

    Real-time spatial-temporal traversability assessment via feature-based sparse gaussian process,

    S. Tan, Z. Hou, Z. Zhang, L. Xu, M. Zhang, Z. He, C. Xu, F. Gao, and Y . Cao, “Real-time spatial-temporal traversability assessment via feature-based sparse gaussian process,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2025, pp. 17 533–17 540

    work page 2025

  6. [6]

    Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments,

    P. Kr ¨usi, P. Furgale, M. Bosse, and R. Siegwart, “Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments,”Journal of Field Robotics, vol. 34, no. 5, pp. 940–984, 2017

    work page 2017

  7. [7]

    Gaitmesh: Controller-aware navigation meshes for long-range legged locomotion planning in multi- layered environments,

    M. Brandao, O. B. Aladag, and I. Havoutis, “Gaitmesh: Controller-aware navigation meshes for long-range legged locomotion planning in multi- layered environments,”IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3596–3603, 2020

    work page 2020

  8. [8]

    Putn: A plane-fitting based uneven terrain navigation framework,

    Z. Jian, Z. Lu, X. Zhou, B. Lan, A. Xiao, X. Wang, and B. Liang, “Putn: A plane-fitting based uneven terrain navigation framework,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 7160–7166

    work page 2022

  9. [9]

    Pare: A plane-assisted autonomous robot exploration framework in unknown and uneven terrain,

    P. Xu, Z. Bai, H. Liu, and Z. Fang, “Pare: A plane-assisted autonomous robot exploration framework in unknown and uneven terrain,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 11 707–11 714

    work page 2024

  10. [10]

    Efficient trajectory generation based on traversable planes in 3d complex archi- tectural spaces,

    M. Zhang, Z. Tian, Y . Xia, C. Xu, F. Gao, and Y . Cao, “Efficient trajectory generation based on traversable planes in 3d complex archi- tectural spaces,” in2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025, pp. 14 513–14 519

    work page 2025

  11. [11]

    Efficient global nav- igational planning in 3-d structures based on point cloud tomography,

    B. Yang, J. Cheng, B. Xue, J. Jiao, and M. Liu, “Efficient global nav- igational planning in 3-d structures based on point cloud tomography,” IEEE/ASME Transactions on Mechatronics, vol. 30, no. 1, 2025

    work page 2025

  12. [12]

    Real- time multilevel terrain-aware path planning for ground mobile robots in large-scale rough terrains,

    Y . Li, K. Chen, Y . Wang, W. Zhang, J. Wang, H. Chen, and Y . Liu, “Real- time multilevel terrain-aware path planning for ground mobile robots in large-scale rough terrains,”IEEE Transactions on Robotics, vol. 41, pp. 4159–4179, 2025

    work page 2025

  13. [13]

    Trg-planner: Traversal risk graph-based path planning in unstructured environments for safe and efficient navigation,

    D. Lee, I. M. A. Nahrendra, M. Oh, B. Yu, and H. Myung, “Trg-planner: Traversal risk graph-based path planning in unstructured environments for safe and efficient navigation,”IEEE Robotics and Automation Letters, vol. 10, no. 2, pp. 1736–1743, 2025

    work page 2025

  14. [14]

    Frontier-based exploration using multiple robots,

    B. Yamauchi, “Frontier-based exploration using multiple robots,” in Proceedings of the second international conference on Autonomous agents, 1998, pp. 47–53

    work page 1998

  15. [15]

    Receding horizon

    A. Bircher, M. Kamel, K. Alexis, H. Oleynikova, and R. Siegwart, “Receding horizon” next-best-view” planner for 3d exploration,” in 2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 1462–1468

    work page 2016

  16. [16]

    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

  17. [17]

    Tare: A hierarchical frame- work for efficiently exploring complex 3d environments

    C. Cao, H. Zhu, H. Choset, and J. Zhang, “Tare: A hierarchical frame- work for efficiently exploring complex 3d environments.” inRobotics: Science and Systems, vol. 5, 2021

    work page 2021

  18. [18]

    Falcon: Fast autonomous aerial exploration using coverage path guidance,

    Y . Zhang, X. Chen, C. Feng, B. Zhou, and S. Shen, “Falcon: Fast autonomous aerial exploration using coverage path guidance,”IEEE Transactions on Robotics, 2024

    work page 2024

  19. [19]

    Racer: Rapid collaborative exploration with a decentralized multi-uav system,

    B. Zhou, H. Xu, and S. Shen, “Racer: Rapid collaborative exploration with a decentralized multi-uav system,”IEEE Transactions on Robotics, 2023

    work page 2023

  20. [20]

    Sfre: Safe and fast robotic exploration for 3d uneven terrains,

    S. Liu, R. Wang, Q. Bi, G. Wen, and X. Zhang, “Sfre: Safe and fast robotic exploration for 3d uneven terrains,” in2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2024, pp. 1–6

    work page 2024

  21. [21]

    Actively mapping industrial structures with information gain-based planning on a quadruped robot,

    Y . Wang, M. Ramezani, and M. Fallon, “Actively mapping industrial structures with information gain-based planning on a quadruped robot,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 7535–7542, 2020

    work page 2020

  22. [22]

    Three-dimensional terrain aware autonomous exploration for subterranean and confined spaces,

    H. Azpurua, M. F. M. Campos, and D. G. Macharet, “Three-dimensional terrain aware autonomous exploration for subterranean and confined spaces,” in2021 IEEE International Conference on Robotics and Au- tomation (ICRA). IEEE, 2021, pp. 2443–2449

    work page 2021

  23. [23]

    Fael: Fast autonomous exploration for large-scale environments with a mobile robot,

    J. Huang, B. Zhou, Z. Fan, Y . Zhu, Y . Jie, L. Li, and H. Cheng, “Fael: Fast autonomous exploration for large-scale environments with a mobile robot,”IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1667– 1674, 2023

    work page 2023

  24. [24]

    Lite: A learning-integrated topological explorer for multi-floor indoor envi- ronments,

    J. Chen, Z. Zhang, C. Zhu, X. Hou, T. Hu, H. Wu, and Y . Liu, “Lite: A learning-integrated topological explorer for multi-floor indoor envi- ronments,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2025, pp. 9533–9540

    work page 2025

  25. [25]

    Unlocking full exploration potential of ground robots by multiresolution topological mapping,

    Y . Jia, C. Wang, W. Tang, Z. Wang, and Z. Sun, “Unlocking full exploration potential of ground robots by multiresolution topological mapping,”IEEE Transactions on Industrial Informatics, vol. 21, no. 8, pp. 6387–6397, 2025

    work page 2025

  26. [26]

    Fast-lio2: Fast direct lidar- inertial odometry,

    W. Xu, Y . Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar- inertial odometry,”IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2053–2073, 2022

    work page 2053