iMAP: Implicit Mapping and Positioning in Real-Time

Edgar Sucar, Shikun Liu, Joseph Ortiz, Andrew J. Davison; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6229-6238


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.

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@InProceedings{Sucar_2021_ICCV, author = {Sucar, Edgar and Liu, Shikun and Ortiz, Joseph and Davison, Andrew J.}, title = {iMAP: Implicit Mapping and Positioning in Real-Time}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {6229-6238} }