3 accepted papers
We present an efficient subpixel refinement method using a learning-based approach called Linear Predictors. Firstly, we present a novel technique, called Symbolic Linear Predictors, which makes the learning step efficient for subpixel refinement. This makes our approach feasible for online applicat…
In this paper, we present MO-SLAM, a novel visual SLAM system that is capable of detecting duplicate objects in the scene during run-time without requiring an offline training stage to pre-populate a database of objects. Instead, we propose a novel method to detect landmarks that belong to duplicate…
This paper presents a monocular visual SLAM system that does not require a globally consistent 3D model. Instead of generating a globally consistent 3D model and localising the camera from the 3D model, the system merely optimises relative pose parameters for pairs of keyframes that overlap on the s…