Pedagogy

Mobile robot

RGB color model

Politics

Operating system

Algorithm

Pixel

Political science

Artificial intelligence

Psychology

Tracking (education)

Mobile device

Representation (politics)

Computer science

Computation

Simultaneous localization and mapping

Computer vision

Artificial neural network

Robot

We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking.Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.

iMAP: Implicit Mapping and Positioning in Real-Time