ISSN 3083-4471 API Registry JSON Sitemap
TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 1 (July 2026) > KAGG:2607.00007
Vol. 1, No. 1 (July 2026) — Article KAGG:2607.00007

Multi-Modal Range Fusion for Agile Quadruped Navigation in Dense Vegetation

Authors: Farah Idris, Hao Chen, Kai Wen Lim

Robotics and Control Laboratory, Nanyang Technological University, Singapore

[Received 2026-07-10; accepted 2026-07-21; published online 2026-07-24]

Abstract:

Quadruped robots are expected to move quickly through forests, parks, and overgrown terrain, yet LiDAR range sensing degrades precisely there: foliage produces premature returns, thin stems and branches fall between beams, and occlusion hides the rigid structure that constrains motion. This paper presents a range-fusion method that combines LiDAR and stereo-vision range estimates with weights conditioned on where the robot is predicted to move. A configuration-driven local planner, in the sense of AgniNav, supplies the robot's collision envelope and candidate trajectories; a first-order model of velocity tracking converts them into a motion-predicted occupancy field over the sensing grid. Per-cell fusion weights combine this field with modality-specific noise, motion-induced latency error, and a vegetation degradation model whose density parameter is estimated online from LiDAR transmittance. The method is evaluated on a Unitree Go2 carrying a stereo rig and a 32-beam LiDAR, in an Isaac Lab synthetic forest suite with four vegetation density classes and on a 600 m park course through shrub and sapling cover. Compared with LiDAR-only sensing, it reduces range estimate error from 0.99 m to 0.94 m, raises mean speed from 2.07 m/s to 2.31 m/s, and raises obstacle-free success from 84.6 to 90.7 percent, while mean obstacle clearance decreases from 18.1 cm to 16.8 cm. Uniform-weight fusion reaches only 84.9 percent success, and removing any single component lowers success. A failure taxonomy covers dense sapling cover and head-height branches.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 1 (July 2026)
Article ID: KAGG:2607.00007
DOI: 10.5803/kagg.2607.00007
Received: 2026-07-10
Accepted: 2026-07-21
Published online: 2026-07-24
License: CC BY 4.0

References (43)

  1. J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, 2019. arXiv:1901.08652. arXiv:1901.08652
  2. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251. arXiv:2010.11251
  3. T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, 2022. arXiv:2201.08117. arXiv:2201.08117
  4. M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. Field Robotics, 2022. arXiv:2201.07067. arXiv:2201.07067
  5. A. Bouman et al.. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. arXiv:2010.09259. arXiv:2010.09259
  6. H. Hirschmüller. Stereo Processing by Semiglobal Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):328-341, 2008. doi:10.1109/TPAMI.2007.1166. doi:10.1109/TPAMI.2007.1166
  7. J.-R. Chang and Y.-S. Chen. Pyramid Stereo Matching Network. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. arXiv:1803.08669. arXiv:1803.08669
  8. L. Lipson, Z. Teed, and J. Deng. RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. International Conference on 3D Vision (3DV), 2021. arXiv:2109.07547. arXiv:2109.07547
  9. T. Zang, S. Cheng, H. Huang, S. Wang, and W. Zhang. AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation. arXiv:2606.10903, 2026. arXiv:2606.10903
  10. N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. Conference on Robot Learning (CoRL), 2021. arXiv:2109.11978. arXiv:2109.11978
  11. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems (RSS), 2021. arXiv:2107.04034. arXiv:2107.04034
  12. A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. Conference on Robot Learning (CoRL), 2022. arXiv:2211.07638. arXiv:2211.07638
  13. R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang. Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. International Conference on Learning Representations (ICLR), 2022. arXiv:2107.03996. arXiv:2107.03996
  14. C. S. Imai et al.. Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2109.14549. arXiv:2109.14549
  15. Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. Conference on Robot Learning (CoRL), 2023. arXiv:2309.05665. arXiv:2309.05665
  16. X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023. arXiv:2309.14341
  17. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874. arXiv:2306.14874
  18. N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2209.12827. arXiv:2209.12827
  19. D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, 2021. arXiv:2103.04351. arXiv:2103.04351
  20. Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. arXiv:2112.02094. arXiv:2112.02094
  21. T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2204.12876. arXiv:2204.12876
  22. G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics and Automation Letters, 2021. arXiv:2002.05700. arXiv:2002.05700
  23. M. V. Gasparino et al.. WayFAST: Navigation with Predictive Traversability in the Field. IEEE Robotics and Automation Letters, 2022. arXiv:2203.12071. arXiv:2203.12071
  24. J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems (RSS), 2023. arXiv:2305.08510. arXiv:2305.08510
  25. M. Mattamala et al.. Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision. arXiv:2404.07110, 2024. arXiv:2404.07110
  26. A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. Science Robotics, 2021. arXiv:2110.05113. arXiv:2110.05113
  27. H. Xi, M. Tan, X. Zhang, S. Cheng, S. Wang, Y. Gu, X. Shen, and W. Zhang. CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation. arXiv:2603.19602, 2026. arXiv:2603.19602
  28. D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. IEEE International Conference on Robotics and Automation (ICRA), 2023. arXiv:2210.03370. arXiv:2210.03370
  29. D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23-33, 1997. doi:10.1109/100.580977. doi:10.1109/100.580977
  30. B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen. Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight. IEEE Robotics and Automation Letters, 2019. arXiv:1907.01531. arXiv:1907.01531
  31. X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao. EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors. IEEE Robotics and Automation Letters, 2021. arXiv:2008.08835. arXiv:2008.08835
  32. X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. Autonomous Robots, 2022. arXiv:2011.13112. arXiv:2011.13112
  33. A. Elfes. Using Occupancy Grids for Mobile Robot Perception and Navigation. Computer, 22(6):46-57, 1989. doi:10.1109/2.30720. doi:10.1109/2.30720
  34. H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. arXiv:1611.03631. arXiv:1611.03631
  35. T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. arXiv:2007.00258. arXiv:2007.00258
  36. W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang. FAST-LIO2: Fast Direct LiDAR-Inertial Odometry. IEEE Transactions on Robotics, 2022. arXiv:2107.06829. arXiv:2107.06829
  37. F. Ma and S. Karaman. Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image. IEEE International Conference on Robotics and Automation (ICRA), 2018. arXiv:1709.07492. arXiv:1709.07492
  38. J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger. Sparsity Invariant CNNs. International Conference on 3D Vision (3DV), 2017. arXiv:1708.06500. arXiv:1708.06500
  39. M. Bijelic et al.. Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. arXiv:1902.08913. arXiv:1902.08913
  40. A. Kendall and Y. Gal. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Advances in Neural Information Processing Systems (NeurIPS), 2017. arXiv:1703.04977. arXiv:1703.04977
  41. V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation for Robot Learning. NeurIPS Datasets and Benchmarks Track, 2021. arXiv:2108.10470. arXiv:2108.10470
  42. M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics and Automation Letters, 2023. arXiv:2301.04195. arXiv:2301.04195
  43. J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. arXiv:1703.06907. arXiv:1703.06907
Cite this article Farah Idris, Hao Chen, Kai Wen Lim, "Multi-Modal Range Fusion for Agile Quadruped Navigation in Dense Vegetation," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 1, KAGG:2607.00007. DOI: 10.5803/kagg.2607.00007.