Neural guided visual slam system with Laplacian of Gaussian operator

Corner Detection Using Laplacian of Gaussian Operator

This paper describes a fast corner detection algorithm making use of the Laplacian of Gaussian operator. We propose a general corner model and analyze its behavior in scale space. The study shows that the response of the operator has a stable elliptic extremum which always lies inside the corner. Using a multi-scale representation limited to two scales and the sole laplacian of Gaussian operato...

Gaussian Multi-Robot SLAM

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous— which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot po...

Relationship between Laplacian Operator and D'alembertian Operator

Laplacian and D’Alembertian operators on functions are very important tools for several branches of Mathematics and Physics. In addition to their relevance, both operators are very used in vector calculus. In this paper, we show a relationship between the Laplacian and the D’Alembertian operators, not only on functions but also on vector fields defined on hypersurfaces in the m-dimensional Lore...

The case for visual-inertial SLAM over visual SLAM

Neural SLAM

We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory architectures, in which the external memory acts as an internal representation of the ...