metadata
license: mit
tags:
- slam
- visual-odometry
- optical-flow
- stereo-matching
- robotics
NeuSLAMNet weights for NeuSLAM
Weights for NeuSLAMNet, the network inside NeuSLAM, a stereo / RGB-D visual SLAM system for embedded GPUs.
From three images, (left_keyframe, right_keyframe, left_current) at 368x768, NeuSLAMNet
predicts in one pass:
- stereo disparity of the keyframe, with a per-pixel confidence;
- optical flow keyframe -> current, with a per-pixel confidence;
- a per-pixel motion logit.
16.4 M parameters.
Files
| file | contents |
|---|---|
neuslamnet.pth |
PyTorch state dict (torch.load(..., weights_only=True)) |
ORBvoc.txt |
ORB vocabulary for DBoW3 loop closure (the ORB-SLAM2 vocabulary) |
reference_engines/neuslamnet_fp16.plan |
the TensorRT fp16 engine (stereo) behind the published results |
reference_engines/neuslamnet_flow_fp16.plan |
its flow-only counterpart (RGB-D) |
Use
pip install ./third_party/pydbow3 && pip install -e . # from the NeuSLAM repository
neuslam download # fetches these files into ~/.cache/neuslam
neuslam build-engine # builds the TensorRT engines for your GPU
Build your own engines with neuslam build-engine: TensorRT plans are specific to
the GPU and the TensorRT version. The reference engines reproduce the published
numbers exactly, but only load with TensorRT 10.15 on a compatible NVIDIA GPU (they
were used on an RTX 3090). Two fp16 builds of the same weights can differ slightly
in their results, so numbers from a local build will be close to, not identical
with, the published ones.