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Fast and accurate path planning is important for ground robots to achievesafe and efficient autonomous navigation in unstructured outdoor environments.However, most existing methods exploiting either 2D or 2.5D maps struggle tobalance the efficiency and safety for ground robots navigating in suchchallenging scenarios. In this paper, we propose a novel hybrid maprepresentation by fusing a 2D grid and a 2.5D digital elevation map. Based onit, a novel path planning method is proposed, which considers the robot posesduring traversability estimation. By doing so, our method explicitly takessafety as a planning constraint enabling robots to navigate unstructuredenvironments smoothly.The proposed approach has been evaluated on bothsimulated datasets and a real robot platform. The experimental resultsdemonstrate the efficiency and effectiveness of the proposed method. Comparedto state-of-the-art baseline methods, the proposed approach consistentlygenerates safer and easier paths for the robot in different unstructuredoutdoor environments. The implementation of our method is publicly available athttps://github.com/nubot-nudt/T-Hybrid-planner.

Hybrid Map-Based Path Planning for Robot Navigation in Unstructured Environments