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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2512.11876 |
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| _version_ | 1866909963290411008 |
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| author | Suryawanshi, Hrigved Mahesh |
| author_facet | Suryawanshi, Hrigved Mahesh |
| contents | Autonomous navigation in unstructured environments requires robots to assess terrain difficulty in real-time and plan paths that balance efficiency with safety. This thesis presents a traversability-aware navigation framework for the M4 robot platform that uses learned terrain analysis to generate energy-efficient paths avoiding difficult terrain.Our approach uses FAST-LIO for real-time localization, generating 2.5D elevation maps from LiDAR point clouds. A CNN-based model processes these elevation maps to estimate traversability scores, which are converted into navigation costs for path planning. A custom A* planner incorporates these costs alongside geometric distance and energy consumption to find paths that trade modest distance increases for substantial terrain quality improvements. Before system development, a platform-agnostic study compared LiDAR-based and camera-based SLAM using OptiTrack ground truth. Point cloud comparison through ICP alignment and cloud-to-mesh distance analysis demonstrated that LiDAR-based mapping achieves centimeter-level precision essential for elevation mapping, while camera-based approaches exhibited significantly higher geometric error. These findings directly resulted in the selection of LiDAR as the primary sensor to generate elevation maps. The complete pipeline integrates FAST-LIO localization, GPU-accelerated elevation mapping, CNN-based traversability estimation, and Nav2 navigation with a custom traversability-aware planner. Experimental results demonstrate that the system successfully avoids low traversability regions and accepts a few longer paths to achieve a reduction in terrain cost. This work establishes a foundation for intelligent terrain-aware navigation applicable to multi-modal robotic platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traversability Aware Autonomous Navigation for Multi-Modal Mobility Morphobot (M4) Suryawanshi, Hrigved Mahesh Robotics Systems and Control Autonomous navigation in unstructured environments requires robots to assess terrain difficulty in real-time and plan paths that balance efficiency with safety. This thesis presents a traversability-aware navigation framework for the M4 robot platform that uses learned terrain analysis to generate energy-efficient paths avoiding difficult terrain.Our approach uses FAST-LIO for real-time localization, generating 2.5D elevation maps from LiDAR point clouds. A CNN-based model processes these elevation maps to estimate traversability scores, which are converted into navigation costs for path planning. A custom A* planner incorporates these costs alongside geometric distance and energy consumption to find paths that trade modest distance increases for substantial terrain quality improvements. Before system development, a platform-agnostic study compared LiDAR-based and camera-based SLAM using OptiTrack ground truth. Point cloud comparison through ICP alignment and cloud-to-mesh distance analysis demonstrated that LiDAR-based mapping achieves centimeter-level precision essential for elevation mapping, while camera-based approaches exhibited significantly higher geometric error. These findings directly resulted in the selection of LiDAR as the primary sensor to generate elevation maps. The complete pipeline integrates FAST-LIO localization, GPU-accelerated elevation mapping, CNN-based traversability estimation, and Nav2 navigation with a custom traversability-aware planner. Experimental results demonstrate that the system successfully avoids low traversability regions and accepts a few longer paths to achieve a reduction in terrain cost. This work establishes a foundation for intelligent terrain-aware navigation applicable to multi-modal robotic platforms. |
| title | Traversability Aware Autonomous Navigation for Multi-Modal Mobility Morphobot (M4) |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2512.11876 |