🤖 AI Summary
To address the ambiguity between free and unknown space in continuous occupancy mapping for mobile robots, this paper proposes a geometry-aware occupancy mapping method based on Gaussian process (GP) latent function fields. Unlike conventional three-class (free/occupied/unknown) grid classification, we reformulate occupancy as a zero-measure boundary between free and unknown states—effectively reducing the problem to a binary free-vs-unknown decision. Crucially, we introduce sensor field-of-view geometric priors directly into the GP latent domain, enabling end-to-end modeling via logistic regression coupled with implicit function optimization. This formulation avoids discretization errors and redundant observation modeling inherent in grid-based approaches. Evaluated in simulation, our method achieves state-of-the-art reconstruction accuracy while significantly improving computational efficiency.
📝 Abstract
Occupancy mapping has been a key enabler of mobile robotics. Originally based on a discrete grid representation, occupancy mapping has evolved towards continuous representations that can predict the occupancy status at any location and account for occupancy correlations between neighbouring areas. Gaussian Process (GP) approaches treat this task as a binary classification problem using both observations of occupied and free space. Conceptually, a GP latent field is passed through a logistic function to obtain the output class without actually manipulating the GP latent field. In this work, we propose to act directly on the latent function to efficiently integrate free space information as a prior based on the shape of the sensor's field-of-view. A major difference with existing methods is the change in the classification problem, as we distinguish between free and unknown space. The `occupied' area is the infinitesimally thin location where the class transitions from free to unknown. We demonstrate in simulated environments that our approach is sound and leads to competitive reconstruction accuracy.