Adaptive Cost Function for Pointcloud Registration
This addresses the problem of pointcloud registration for robotics and computer vision applications, offering an incremental improvement by adapting to sensor noise.
The paper tackles pointcloud registration by introducing an adaptive cost function that automatically estimates sensor noise, leading to significant improvements in accuracy and robustness over state-of-the-art methods in experiments on real and synthetic data.
In this paper we introduce an adaptive cost function for pointcloud registration. The algorithm automatically estimates the sensor noise, which is important for generalization across different sensors and environments. Through experiments on real and synthetic data, we show significant improvements in accuracy and robustness over state-of-the-art solutions.