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arxiv: 2511.01219 · v7 · pith:44SC7LUSnew · submitted 2025-11-03 · 💻 cs.RO

Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference

Muhua Zhang , Lei Ma , Ying Wu , Kai Shen , Deqing Huang , Henry Leung This is my paper

Pith reviewed 2026-05-21 20:56 UTC · model grok-4.3

classification 💻 cs.RO
keywords kidnapped robot problemglobal relocalizationLiDAR localizationRRT hypothesis samplingmulti-hypothesis inferenceoccupancy grid mapmobile robot navigation
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The pith

A framework using sparse RRT hypothesis sampling and multi-stage inference solves the kidnapped robot problem from a single LiDAR scan.

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

The paper develops a method to relocalize a mobile robot in a known map after it loses its position estimate, known as the kidnapped robot problem. It does this by sampling a limited number of possible positions that respect the map's traversable areas using a random tree approach, then checking them in batches with early stopping to find the correct pose efficiently. This allows the robot to determine its location while staying still, using only one scan from a limited-field LiDAR sensor. The method introduces specific metrics to rank and refine candidates quickly, making it suitable for robots with limited computing power.

Core claim

The central claim is that global relocalization can be achieved efficiently by casting it as a non-convex problem solved through a multi-hypothesis scheme that employs RRT to generate sparse uniformly distributed feasible positional hypotheses under traversability constraints, orders them with the SMAD metric for early termination, and uses the TAM metric for orientation selection and final evaluation to handle uncertainties from sparse sampling and non-panoramic scans.

What carries the argument

The multi-hypothesis scheme with batched multi-stage inference, driven by RRT-based sparse feasible positional hypotheses, SMAD coarse ordering, and TAM for reliable alignment and evaluation.

If this is right

  • The robot achieves competitive success rates in real-world tests with non-panoramic LiDAR.
  • Early termination balances completeness and computational efficiency on resource-constrained hardware.
  • The framework supports long-term autonomy by enabling reliable relocalization without prior pose.
  • Robustness is maintained under measurement uncertainty and environmental changes.

Where Pith is reading between the lines

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

  • Applying this to updating maps in mildly dynamic settings could extend its use beyond static environments.
  • Integration with SLAM initialization might reduce drift in large-scale mapping tasks.
  • Comparing the method's performance against particle filter baselines in simulation could highlight efficiency gains.

Load-bearing premise

The method depends on an accurate static occupancy grid map being available to define reliable traversability constraints for generating hypotheses.

What would settle it

Experiments showing that the success rate drops significantly when the provided map has inaccuracies or when the robot is in areas with poor traversability definition would falsify the reliability claims.

Figures

Figures reproduced from arXiv: 2511.01219 by Deqing Huang, Henry Leung, Kai Shen, Lei Ma, Muhua Zhang, Ying Wu.

Figure 1
Figure 1. Figure 1: The entire pipeline of the proposed framework. Algorithm 1 Traversability-Constrained Hypothesis Sampling 1: Input: Map origin 𝒑଴ , RRT tree 𝑻ோோ், RRT sampling boundary 𝓑, grid map 𝓜, map width 𝑊ℳ, map height 𝐻ℳ, map resolution 𝑟ℳ, sampling spacing 𝜌, and gain threshold 𝜀௚௔௜௡ 2: Output: Sparse hypotheses 𝓟 = {𝒑௝ }௝ୀଵ ே𝒑 with 𝒑௝ ∈ ℛ(𝒑଴) 3: Initialize: 𝑻𝑹𝑹𝑻 ← {𝒑0 }, 𝓟 ← ∅, 𝓟௟௔௦௧ ← ∅ 4: 𝑁𝒔𝒂𝒎𝒑𝒍𝒊𝒏𝒈 ← ⌈𝑊ℳ𝐻ℳ𝑟ℳ ଶ … view at source ↗
Figure 3
Figure 3. Figure 3: Accelerated SMAD calculation via prefix sum of ranges from the panoramic map-synthesized scan. 𝑀𝑖𝑛𝐷𝑖𝑠𝑡𝑇𝑜𝑂𝑏𝑠𝑡𝑎𝑐𝑙𝑒(𝒑, 𝓜) . When 𝑑௢௕௦ ≥ 2𝑟௥௢௕௢௧ , 𝜂 = 𝜂௠௔௫ , the maximum expansion distance of RRT. When 𝑑௢௕௦ ≤ 𝑟௥௢௕௢௧ , 𝜂 = 𝑟௥௢௕௢௧ .When 𝑟௥௢௕௢௧ ≤ 𝑑௢௕௦ ≤ 2𝑟௥௢௕௢௧, 𝜂 decreases linearly as 𝑑௢௕௦ decreases. 6) TraversabilityCheck: Verifies whether all points 𝒑 on the line segment [𝒑଴ , 𝒑ଵ ] satisfy 𝑑௢௕௦(𝒑) ≥ 𝑟௥௢௕௢௧. 7)… view at source ↗
Figure 4
Figure 4. Figure 4: Typical degradation cases of likelihood-field-based scan-to-map alignment metrics for relocalization tasks. 𝑆[𝑡] = ෍𝓏̂௝ ᇱ(𝒑) ௧ ௝ୀଵ , 𝑡 ∈ [1,2𝑁௦ ], (17) where {𝓏̂௝ ᇱ(𝒑)} is duplicated once to form the cyclic extension. Then, the mean range of the synthesized scan under orientation 𝜃௠ is computed in constant time, as shown in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experimental robot and configurations of real-world experimental scenarios [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Typical degradation cases of likelihood-field-based scan-to-map alignment metrics for relocalization tasks [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Spatial SMAD Heatmaps of four relocalization cases in the scenario of [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

This paper addresses the Kidnapped Robot Problem (KRP), a core localization challenge of relocalizing a robot in a known map without prior pose estimate upon localization loss or at SLAM initialization. For this purpose, a passive 2-D global relocalization framework is proposed. It estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots. The proposed framework casts global relocalization as a non-convex problem and solves it via the multi-hypothesis scheme with batched multi-stage inference and early termination, balancing completeness and efficiency. The Rapidly-exploring Random Tree (RRT), under traversability constraints, asymptotically covers the reachable space to generate sparse, uniformly distributed feasible positional hypotheses, fundamentally reducing the sampling space. The hypotheses are preliminarily ordered by the proposed Scan Mean Absolute Difference (SMAD), a coarse beam-error level metric that facilitates the early termination by prioritizing high-likelihood candidates. The SMAD computation is optimized for limited scan measurements. The Translation-Affinity Scan-to-Map Alignment Metric (TAM) is proposed for reliable orientation selection at hypothesized positions and accurate final global pose evaluation to mitigate degradation in conventional likelihood-field metrics under translational uncertainty induced by sparse hypotheses, as well as non-panoramic LiDAR scan and environmental changes. Real-world experiments on a resource-constrained mobile robot with non-panoramic LiDAR scans show that the proposed framework achieves competitive performance in success rate, robustness under measurement uncertainty, and computational efficiency.

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

1 major / 2 minor

Summary. The paper addresses the Kidnapped Robot Problem by proposing a passive 2-D global relocalization framework that generates sparse feasible positional hypotheses via RRT under traversability constraints from a known occupancy grid map, orders them with the new SMAD metric for early termination in batched multi-stage inference, and uses the TAM metric for final orientation selection and pose evaluation. It reports competitive success rate, robustness to measurement uncertainty, and computational efficiency from real-world experiments on a resource-constrained mobile robot using non-panoramic LiDAR scans.

Significance. If the empirical results hold, the work provides a practical, efficient solution for long-term robot autonomy in localization-loss scenarios by reducing the search space with feasible RRT sampling and introducing SMAD and TAM metrics tailored to sparse non-panoramic scans. The real-robot experiments on limited hardware and the explicit handling of translational uncertainty are strengths that could influence practical SLAM and relocalization systems.

major comments (1)
  1. [Abstract and §3] Abstract and §3 (hypothesis generation): The central performance claims rest on RRT sampling under traversability constraints derived from an assumed accurate static occupancy grid map. Any map inaccuracy (outdated obstacles or sensor-derived errors) would exclude the true pose from the sparse hypothesis set, invalidating the subsequent SMAD ordering, multi-stage inference, and TAM evaluation. The manuscript should either provide robustness analysis under map perturbations or explicitly bound the assumption's impact on the reported success rates.
minor comments (2)
  1. [Abstract] The definitions and optimization details for the SMAD and TAM metrics (introduced in the abstract) would benefit from explicit equations or pseudocode to allow reproduction and comparison with likelihood-field baselines.
  2. [Method description] Clarify the exact termination thresholds and batch sizes used in the multi-stage inference, as post-hoc tuning could affect the claimed efficiency-robustness trade-off.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below, providing clarifications on our assumptions while committing to revisions that improve transparency without misrepresenting the work.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (hypothesis generation): The central performance claims rest on RRT sampling under traversability constraints derived from an assumed accurate static occupancy grid map. Any map inaccuracy (outdated obstacles or sensor-derived errors) would exclude the true pose from the sparse hypothesis set, invalidating the subsequent SMAD ordering, multi-stage inference, and TAM evaluation. The manuscript should either provide robustness analysis under map perturbations or explicitly bound the assumption's impact on the reported success rates.

    Authors: We agree that the framework's hypothesis generation relies on an accurate static occupancy grid map to define traversability constraints for RRT sampling. This is a standard assumption in map-based global relocalization and kidnapped robot problem literature, as the method is designed for passive relocalization given a known prior map rather than simultaneous mapping. In our real-world experiments, maps were constructed via prior SLAM runs and manually verified for fidelity to the test environments. To address the concern, we will revise §3 to explicitly state this assumption and add a dedicated paragraph bounding its impact: we will clarify that the reported success rates hold when map errors are smaller than the robot's footprint (ensuring the true pose remains traversable), and that larger discrepancies (e.g., outdated obstacles) could indeed exclude valid hypotheses. We will also note that the framework targets static or slowly changing environments and that dynamic map maintenance lies outside the current scope. This revision makes the limitations transparent while preserving the validity of the empirical results under the stated conditions. revision: yes

Circularity Check

0 steps flagged

No circularity: framework validated by independent real-robot experiments

full rationale

The paper presents a multi-hypothesis relocalization method that generates positional candidates via RRT under map-derived traversability constraints, ranks them with the explicitly defined SMAD metric, and refines with the new TAM alignment score. All performance claims are tied to external real-world trials on a resource-constrained robot with non-panoramic LiDAR, rather than any internal fit or self-referential derivation. No equations, predictions, or uniqueness theorems are shown to reduce to the method's own inputs by construction, and no load-bearing self-citations appear in the provided description. The approach is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the availability of a known accurate map and the ability to define traversability for sampling; no explicit free parameters or new physical entities are named in the abstract.

axioms (2)
  • domain assumption A known static occupancy grid map is available and traversability constraints can be extracted from it for RRT sampling.
    Invoked in the description of generating feasible positional hypotheses.
  • domain assumption The robot remains stationary during the single-scan relocalization process.
    Stated explicitly as the operating condition for the passive framework.
invented entities (2)
  • SMAD (Scan Mean Absolute Difference) metric no independent evidence
    purpose: Coarse beam-error metric to order hypotheses and enable early termination
    Newly proposed in the paper for preliminary ranking of candidates.
  • TAM (Translation-Affinity Scan-to-Map Alignment Metric) no independent evidence
    purpose: Reliable orientation selection and final pose evaluation under translational uncertainty
    Newly proposed to mitigate issues with conventional likelihood-field metrics.

pith-pipeline@v0.9.0 · 5830 in / 1567 out tokens · 50949 ms · 2026-05-21T20:56:02.202108+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Offline-Online Hierarchical 3D Global Relocalization With Synthetic LiDAR Sensing and Descriptor-Space Retrieval

    cs.RO 2026-05 unverdicted novelty 4.0

    A hierarchical offline-online framework for 3D global relocalization using synthetic LiDAR and descriptor retrieval achieves 3-second average time and 8 cm accuracy with order-of-magnitude efficiency gains over prior methods.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    An Automatic Analog Instrument Reading System Using Computer Vision and Inspection Robot,

    J. Huang, J. Wang, Y. Tan, D. Wu and Y. Cao, "An Automatic Analog Instrument Reading System Using Computer Vision and Inspection Robot," in IEEE Transactions on Instrumentation and Measurement , vol. 69, no. 9, pp. 6322-6335, Sept. 2020

    work page 2020

  2. [2]

    Graph-Based SLAM for Under- Train Inspection Robots in Narrow Site,

    M. Zhang, L. Ma, K. Shen and Y. Sun, "Graph-Based SLAM for Under- Train Inspection Robots in Narrow Site," 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) , Leeds, United Kingdom, 2024, pp. 1-5

    work page 2024

  3. [3]

    A Hybrid- Dimensional Laser SLAM Framework for Indoor Quadruped Inspection Robots,

    J. Cheng, M. Zhang, L. Ma, H. Chen, Y. Gan and D. Huang, "A Hybrid- Dimensional Laser SLAM Framework for Indoor Quadruped Inspection Robots," in IEEE Sensors Journal, vol. 24, no. 10, pp. 16935-16942, 15 May15, 2024

    work page 2024

  4. [4]

    A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers,

    J. A. Placed et al., "A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers," in IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1686-1705, June 2023

    work page 2023

  5. [5]

    Finite Memory- Simultaneous Localization and Calibration With Application to Mobile Robots,

    D. K. Lee, J. M. Pak, P. Shi and C. K. Ahn, "Finite Memory- Simultaneous Localization and Calibration With Application to Mobile Robots," in IEEE Transactions on Industrial Electronics , vol. 72, no. 6, pp. 6145-6154, June 2025

    work page 2025

  6. [6]

    Indoor Localization Uncertainty Control Based on Wireless Ranging for Robots Path Planning,

    F. Shamsfakhr, A. Antonucci, L. Palopoli, D. Macii and D. Fontanelli, "Indoor Localization Uncertainty Control Based on Wireless Ranging for Robots Path Planning," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022

    work page 2022

  7. [7]

    High- Traversability and Precise Navigation for Mobile Robots in Constrained Environments,

    M. Zhang, L. Ma, Y. Wu, K. Shen, Y. Sun and H. Leung, "High- Traversability and Precise Navigation for Mobile Robots in Constrained Environments," in IEEE Sensors Journal , vol. 25, no. 12, pp. 22815- 22826, 15 June15, 2025

    work page 2025

  8. [8]

    Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot,

    F. Guo, H. Yang, X. Wu, H. Dong, Q. Wu and Z. Li, "Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot," in IEEE Transactions on Industrial Electronics , vol. 71, no. 7, pp. 7531- 7541, July 2024

    work page 2024

  9. [9]

    Robust Vision-Aided Inertial Navigation System for Protection Against Ego-Motion Uncertainty of Unmanned Ground Vehicle,

    C. Zhai, M. Wang, Y. Yang and K. Shen, "Robust Vision-Aided Inertial Navigation System for Protection Against Ego-Motion Uncertainty of Unmanned Ground Vehicle," in IEEE Transactions on Industrial Electronics, vol. 68, no. 12, pp. 12462-12471, Dec. 2021

    work page 2021

  10. [10]

    Robust Lifelong Indoor LiDAR Localization Using the Area Graph,

    F. Xie and S. Schwertfeger, "Robust Lifelong Indoor LiDAR Localization Using the Area Graph," in IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 531-538, Jan. 2024

    work page 2024

  11. [11]

    Long-Term Localization Using Semantic Cues in Floor Plan Maps,

    N. Zimmerman, T. Guadagnino, X. Chen, J. Behley and C. Stachniss, "Long-Term Localization Using Semantic Cues in Floor Plan Maps," in IEEE Robotics and Automation Letters , vol. 8, no. 1, pp. 176-183, Jan. 2023

    work page 2023

  12. [12]

    Robust Onboard Localization in Changing Environments Exploiting Text Spotting,

    N. Zimmerman, L. Wiesmann, T. Guadagnino, T. Läbe, J. Behley and C. Stachniss, "Robust Onboard Localization in Changing Environments Exploiting Text Spotting," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 917-924

    work page 2022

  13. [13]

    A Novel UWB/IMU/Odometer-Based Robot Localization System in LOS/NLOS Mixed Environments,

    J. Sun, W. Sun, J. Zheng, Z. Chen, C. Tang and X. Zhang, "A Novel UWB/IMU/Odometer-Based Robot Localization System in LOS/NLOS Mixed Environments," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13, 2024

    work page 2024

  14. [14]

    Sensors applied to automated guided vehicle position control: A systematic literature review,

    W. P. N. dos Reis and O. Morandin Junior, "Sensors applied to automated guided vehicle position control: A systematic literature review," The International Journal of Advanced Manufacturing Technology, vol. 113, no. 1, pp. 21–34, 2021

    work page 2021

  15. [15]

    ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using Lightweight Polygon Maps,

    H. Gao, Q. Qiu, H. Liu, D. Liang, C. Wang and X. Zhang, "ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using Lightweight Polygon Maps," in IEEE Transactions on Robotics, vol. 41, pp. 3799-3819, 2025

    work page 2025

  16. [16]

    Monte Carlo localization for mobile robots,

    F. Dellaert, D. Fox, W. Burgard and S. Thrun, "Monte Carlo localization for mobile robots," Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C) , Detroit, MI, USA, 1999, pp. 1322-1328 vol.2

    work page 1999

  17. [17]

    Development of a new technique in ROS for mobile robots localization in known-based 2D environments,

    I. Hatem and M. A. A. Khalil, “Development of a new technique in ROS for mobile robots localization in known-based 2D environments,” Tishreen Univ. J. Res. Sci. Stud. Eng. Sci. Ser., vol. 43, pp. 1–19, 2021

    work page 2021

  18. [18]

    An Improved Localization of Mobile Robotic System Based on AMCL Algorithm,

    M. -A. Chung and C. -W. Lin, "An Improved Localization of Mobile Robotic System Based on AMCL Algorithm," in IEEE Sensors Journal, vol. 22, no. 1, pp. 900-908, 1 Jan.1, 2022

    work page 2022

  19. [19]

    IR-MCL: Implicit Representation-Based Online Global Localization,

    H. Kuang, X. Chen, T. Guadagnino, N. Zimmerman, J. Behley and C. Stachniss, "IR-MCL: Implicit Representation-Based Online Global Localization," in IEEE Robotics and Automation Letters , vol. 8, no. 3, pp. 1627-1634, March 2023. 11

    work page 2023

  20. [20]

    Research on Mobile Robot Localization Method Based on Adaptive Motion Model and Double-Threshold Relocation Strategy,

    X. Xu et al., "Research on Mobile Robot Localization Method Based on Adaptive Motion Model and Double-Threshold Relocation Strategy," in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1- 12, 2025

    work page 2025

  21. [21]

    Real-time loop closure in 2D LIDAR SLAM,

    W. Hess, D. Kohler, H. Rapp and D. Andor, "Real-time loop closure in 2D LIDAR SLAM," 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016, pp. 1271-1278

    work page 2016

  22. [22]

    Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration,

    J. Yang, H. Li, D. Campbell and Y. Jia, "Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 11, pp. 2241- 2254, 1 Nov. 2016

    work page 2016

  23. [23]

    rviz/UserGuide,

    “rviz/UserGuide,” ROS Wiki, last modified Aug. 31, 2020. [Online]. Available: https://wiki.ros.org/rviz/UserGuide. Accessed: Sep. 30, 2025

    work page 2020

  24. [24]

    Extracting Statistical Signatures of Geometry and Structure in 2D Occupancy Grid Maps for Global Localization,

    S. -Y. An and J. Kim, "Extracting Statistical Signatures of Geometry and Structure in 2D Occupancy Grid Maps for Global Localization," in IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4291-4298, April 2022

    work page 2022

  25. [25]

    Active exploration for feature based global localization,

    M. Seiz, P. Jensfelt and H. I. Christensen, "Active exploration for feature based global localization," Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113), Takamatsu, Japan, 2000, pp. 281-287 vol.1

    work page 2000

  26. [26]

    Active global localization for a mobile robot using multiple hypothesis tracking,

    P. Jensfelt and S. Kristensen, "Active global localization for a mobile robot using multiple hypothesis tracking," in IEEE Transactions on Robotics and Automation, vol. 17, no. 5, pp. 748-760, Oct. 2001

    work page 2001

  27. [27]

    A Hybrid Active Global Localisation Algorithm for Mobile Robots,

    A. Gasparri, S. Panzieri, F. Pascucci and G. Ulivi, "A Hybrid Active Global Localisation Algorithm for Mobile Robots," Proceedings 2007 IEEE International Conference on Robotics and Automation , Rome, Italy, 2007, pp. 3148-3153

    work page 2007

  28. [28]

    Efficient active global localization for mobile robots operating in large and cooperative environments,

    A. C. Murtra, J. M. Mirats Tur and A. Sanfeliu, "Efficient active global localization for mobile robots operating in large and cooperative environments," 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 2008, pp. 2758-2763

    work page 2008

  29. [29]

    Active global localization based on localizability for mobile robots,

    Y. Wang, W. Chen, J. Wang, and H. Wang, “Active global localization based on localizability for mobile robots,” Robotica, vol. 33, no. 8, pp. 1609–1627, 2015

    work page 2015

  30. [30]

    Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities,

    K. P. Cop, P. V. K. Borges and R. Dubé, "Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities," 2018 IEEE International Conference on Robotics and Automation (ICRA) , Brisbane, QLD, Australia, 2018, pp. 3653-3660

    work page 2018

  31. [31]

    Efficient and Reliable LiDAR-Based Global Localization of Mobile Robots Using Multiscale/Resolution Maps,

    J. Meng et al ., "Efficient and Reliable LiDAR-Based Global Localization of Mobile Robots Using Multiscale/Resolution Maps," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1- 15, 2021

    work page 2021

  32. [32]

    Passive global localisation of mobile robot via 2D Fourier-Mellin invariant matching,

    A. Filotheou, A. Tzitzis, E. Tsardoulias, A. Dimitriou, A. Symeonidis, G. Sergiadis, and L. Petrou, “Passive global localisation of mobile robot via 2D Fourier-Mellin invariant matching,” Journal of Intelligent & Robotic Systems, vol. 104, no. 2, p. 26, 2022

    work page 2022

  33. [33]

    CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor,

    A. Filotheou, "CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor," 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Abu Dhabi, United Arab Emirates, 2024, pp. 3268-3275. Muhua Zhang (Graduate Student Member, IEEE) received the bachelor’s degree in mechanical design, manufacturing, and automation from So...

    work page 2024