An efficient probabilistic surface normal estimator
An efficient surface normal estimation method is presented. The new algorithm estimates surface normal direction for each cell in a grid based on the occupancy information (both occupied and empty) of the neighboring cells. This grid representation allows user-defined sizes and scaling with the environment, not the number of measurements. Recursive and batch formulations to obtain the posterior estimate are presented, and compared. A computationally efficient implementation is derived which provides consistent and accurate estimates as measurements become available. Both simulation and experimental results are shown, demonstrating comparable estimation performance to that of using Point Cloud Library, but with significantly reduced computation time.