GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
This work addresses the challenge of efficient and globally consistent dense SLAM for robotics and AR/VR applications, representing an incremental improvement over existing methods.
The authors tackled the problem of achieving globally consistent dense SLAM using only RGB input by proposing a system with a neural point cloud representation and a novel bundle adjustment layer, resulting in competitive or better performance in tracking, mapping, and rendering accuracy on datasets like Replica, TUM-RGBD, and ScanNet.
Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently realize global map and pose consistency. To this end, we propose an efficient RGB-only dense SLAM system using a flexible neural point cloud scene representation that adapts to keyframe poses and depth updates, without needing costly backpropagation. Another critical challenge of RGB-only SLAM is the lack of geometric priors. To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth. Finally, our system benefits from loop closure and online global bundle adjustment and performs either better or competitive to existing dense neural RGB SLAM methods in tracking, mapping and rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is available at https://github.com/zhangganlin/GlOIRE-SLAM