Related papers: Dense RGB SLAM with Neural Implicit Maps

Dense RGB SLAM with Neural Implicit Maps

URL: http://arxiv.org/abs/2301.08930v1
Date: Sat, 21 Jan 2023 09:54:07 GMT
Title: Dense RGB SLAM with Neural Implicit Maps
Authors: Heng Li, Xiaodong Gu, Weihao Yuan, Luwei Yang, Zilong Dong, Ping Tan
Abstract summary: We present a dense RGB SLAM method with neural implicit map representation. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods.
Score: 34.37572307973734
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods. Our source code will be publicly available.

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