Section: New Results

3D Surface Reconstruction from Voxel-based Lidar Data

Participants : Luis Roldao, Raoul de Charette, Anne Verroust-Blondet.

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surroundings. In fact, imprecise representations may lead to unexpected situations that could endanger the passengers. This year, we have proposed an algorithm capable to perform a fine and accurate 3D surface reconstruction of the environment from depth sensors. This representation keeps a high level of detail on the reconstruction, while maintaining a high density in the areas close to the vehicle.

Existing methods used for surface reconstruction from 3D data struggle to accommodate to the heterogeneous density of the input data while keeping the reconstruction accuracy. Conversely, our method is capable of handling this variable density by using an adaptive neighborhood kernel that perform local approximations of the data at different levels. This also permit to gain robustness against noise and output a smoother reconstruction. We also introduce a Gaussian confidence function capable to select the most adequate kernel for the local surface estimation. A Truncated Signed Distance Function (TSDF) is then globally estimated from the local surfaces to obtain the final mesh that represents the input scan.

The proposed method was evaluated in both simulated and real data. Reconstruction results show an improvement on the representation when compared with popular methods such as Implicit Moving Least Squares (IMLS), as the average error of our reconstruction is often 50% lower. Furthermore, almost 80% of vertices from our output mesh present an error below 0.2m, while only 40% of vertices lie below the same threshold for IMLS. Our method is capable to output a higher level of detail on the reconstruction, while keeping a high density in vehicle surroundings, the mesh can be of special interest for both the robotics and the graphics community to perform different tasks, such as terrain traversability assessment or physical modeling.

More details can be found in [21]. This research is partially funded by AKKA Technology.