Neural Elevation Models for Terrain Mapping and Path Planning
<|reference_start|>Neural Elevation Models for Terrain Mapping and Path Planning: This work introduces Neural Elevations Models (NEMos), which adapt Neural Radiance Fields to a 2.5D continuous and differentiable terrain model. In contrast to traditional terrain representations such as digital elevation models, NEMos can be readily generated from imagery, a low-cost data source, and provide a lightweight representation of terrain through an implicit continuous and differentiable height field. We propose a novel method for jointly training a height field and radiance field within a NeRF framework, leveraging quantile regression. Additionally, we introduce a path planning algorithm that performs gradient-based optimization of a continuous cost function for minimizing distance, slope changes, and control effort, enabled by differentiability of the height field. We perform experiments on simulated and real-world terrain imagery, demonstrating NEMos ability to generate high-quality reconstructions and produce smoother paths compared to discrete path planning methods. Future work will explore the incorporation of features and semantics into the height field, creating a generalized terrain model.<|reference_end|>
@article{dai2024neural,
    title={Neural Elevation Models for Terrain Mapping and Path Planning},
    author={Adam Dai, Shubh Gupta, and Grace Gao},
    journal={arXiv preprint arXiv:2405.15227},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15227},
    primaryClass={cs.RO}
}