Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems

Engineering notes

The code will be available at https://www.github.com/norbertmarko/terrain-depth.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The prediction of terrain elevation values is a key task when it comes to off-road dynamics and inertial data estimation. A reliable elevation map can help in the estimation of future vehicle states and thus extend the response time window for autonomous navigation and control. We trained a deep learning model that is able to successfully predict top-down terrain depth maps in an off-road setting using a lightweight monocular depth estimation network. The labels were generated using a custom preprocessing algorithm to aid single image depth model training. Unlike other elevation estimation algorithms, our work can predict terrain variation from a higher camera setting without the use of a multi-sensor system. The network is also shown to work outside of the training data domain. The code will be available at https://www.github.com/norbertmarko/terrain-depth.

Links and sources

• Official / arXiv page

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