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Deep Patch Visual SLAM
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Recent work in visual SLAM has shown the effectiveness of using deep network backbones. Despite excellent accuracy, however, such approaches are often expensive to run or do not generalize well zero-shot. Their runtime can also fluctuate wildly while their frontend and backend fight for access to GPU resources. To address these problems, we introduce Deep Patch Visual (DPV) SLAM, a method for monocular visual SLAM on a single GPU. DPV-SLAM maintains a high minimum framerate and small memory overhead (5-7G) compared to existing deep SLAM systems. On real-world datasets, DPV-SLAM runs at 1x-4x real-time framerates. We achieve comparable accuracy to DROID-SLAM on EuRoC and TartanAir while running 2.5x faster using a fraction of the memory. DPV-SLAM is an extension to the DPVO visual odometry system; its code can be found in the same repository: https://github.com/princeton-vl/DPVO
Forward citations
Cited by 1 Pith paper
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Princeton365: A Diverse Dataset with Accurate Camera Pose
Princeton365 is a 365-video SLAM/NVS benchmark with board-calibrated millimeter-accurate 6-DoF poses, a new scale-aware optical-flow error metric, and an NVS benchmark of fully non-Lambertian 360-degree scans.
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