Probabilistic Multi-Sensor Fusion Based On Signed Distance Functions

In this paper, we present an approach for the probabilistic fusion of 3D sensor measurements. Our fusion algorithm is based on truncated signed distance functions. It explicitly considers the measurement noise by modeling the surface using random variables. Furthermore, our proposed surface model provides an explicit estimation of the spatial uncertainty. The approach can be implemented on a GPU to achieve a high update performance and enable online updates of the model. The approach was evaluated in simulation and using real sensor data. In our experiments, we confirmed that it accurately estimates surfaces from noisy sensor data and that it provides a corresponding estimate of the uncertainty. We could also show that the approach is able to fuse measurements from sensors with different noise characteristics.