Gaussian Process Distance Field

Gaussian Process Distance Fields (GPDFs) represent 3D environments as continuous probability distributions of distances to surfaces, offering a robust and uncertainty-aware alternative to traditional methods. Current research focuses on improving computational efficiency, particularly for large-scale applications, often through integration with optimized data structures like VDB trees. This probabilistic approach enhances accuracy in tasks like robotic grasping and mapping, especially in noisy or dynamic settings, by providing both distance estimates and associated uncertainties. The resulting improvements in precision and reliability are impacting fields ranging from robotics and autonomous navigation to non-destructive testing using techniques like ultrasonic echolocation.