| --- |
| license: mit |
| tags: |
| - slam |
| - visual-odometry |
| - optical-flow |
| - stereo-matching |
| - robotics |
| --- |
| |
| # NeuSLAMNet weights for NeuSLAM |
|
|
| Weights for **NeuSLAMNet**, the network inside [NeuSLAM](https://github.com/aniket-gupta1/Dynamic_SLAM), |
| 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 |
|
|
| ```bash |
| 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. |
|
|
