4 accepted papers
Large scale active exploration has recently revealed limitations of visual SLAM’s tracking ability. Active view planning methods based on reinforcement learning have been proposed to improve visual tracking robustness.In this work, we expose the limitations of deep reinforcement learning-based visua…
Similar appearing places are known to confuse place recognition methods (a.k.a perceptual aliasing). Robust visual SLAM methods have shown impressive resilience against scenarios having moderate perceptual aliasing. In this work, we evaluate visual SLAM in scenarios where perceptual aliasing occurs
Nowadays real time visual Simultaneous Localization And Mapping (SLAM) algorithms exist and rely on consistent measurements across multiple views. In indoor environments, where majority of robot's activity takes place, severe occlusions can occur, e.g., when turning around a corner or moving from on…
Do we really need 3D labels in order to learn how to predict 3D? In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label. Rather than use explicit supervision, we use the regularity of indoor scenes to learn the mapping in a…