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.