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SLAIM: Robust Dense Neural SLAM for Online Tracking and Mapping
April 18, 2024, 4:44 a.m. | Vincent Cartillier, Grant Schindler, Irfan Essa
cs.CV updates on arXiv.org arxiv.org
Abstract: We present SLAIM - Simultaneous Localization and Implicit Mapping. We propose a novel coarse-to-fine tracking model tailored for Neural Radiance Field SLAM (NeRF-SLAM) to achieve state-of-the-art tracking performance. Notably, existing NeRF-SLAM systems consistently exhibit inferior tracking performance compared to traditional SLAM algorithms. NeRF-SLAM methods solve camera tracking via image alignment and photometric bundle-adjustment. Such optimization processes are difficult to optimize due to the narrow basin of attraction of the optimization loss in image space (local …
abstract algorithms art arxiv cs.cv localization mapping nerf neural radiance field novel performance robust slam solve state systems tracking type
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