adityamwagh/SuperSLAM 是一个托管在 GitHub 上的开源项目，主要使用 C++ 编写，采用 GNU Lesser General Public License v2.1 许可证，目前获得 184 个 Star、17 次 Fork。
仓库全名
adityamwagh/SuperSLAM
开源许可证
GNU Lesser General Public License v2.1
Star 数
184
Fork 数
17
README 摘要
SuperSLAM: Accurate Real-Time SLAM with Deep Learned Features Alpha software. SuperSLAM is a real-time stereo and RGB-D visual SLAM system. SuperPoint detects features and LightGlue matches them on a TensorRT FP16 backend, with a GTSAM optimization core and pose-graph loop closure. Results Run on an NVIDIA RTX PRO 1000 (Blackwell, laptop, 8 GB) with an Intel Core Ultra 5 235H, TensorRT 10.11, CUDA 12.9, FP16. fps is…
SuperSLAM: Accurate Real-Time SLAM with Deep Learned Features
Alpha software.
SuperSLAM is a real-time stereo and RGB-D visual SLAM system. SuperPoint detects features and
LightGlue matches them on a TensorRT FP16 backend, with a GTSAM optimization core and pose-graph
loop closure.
Results
Run on an NVIDIA RTX PRO 1000 (Blackwell, laptop, 8 GB) with an Intel Core Ultra 5 235H, TensorRT
10.11, CUDA 12.9, FP16. fps is the per-frame tracking rate (front-end plus window smoother); every
sequence runs above its camera rate (KITTI 10 Hz, EuRoC 20 Hz, TUM 30 Hz). ATE is SE3-aligned RMSE.
Estimated trajectory (red) against ground truth (black). KITTI is shown in the x-z ground plane.
KITTI (stereo, window 10)
seq
ATE RMSE (m)
ATE mean (m)
RPE RMSE (m)
t_rel (%)
r_rel (deg/m)
fps
00
1.582
1.238
0.398
0.76
0.0034
79
01
375.893
340.771
2.607
46.10
0.0322
48
02
7.908
5.945
0.395
1.02
0.0034
71
03
2.291
1.687
0.290
4.66
0.0153
79
04
0.646
0.575
0.121
0.72
0.0012
72
05
4.187
2.719
0.438
1.19
0.0046
71
06
2.222
1.954
0.468
1.56
0.0079
68
07
15.857
8.421
3.935
6.51
0.0356
77
08
6.946
5.897
0.286
1.34
0.0047
73
09
3.991
3.626
0.095
1.80
0.0065
71
10
2.113
1.745
0.089
0.79
0.0063
78
Sequence 01 is the highway: distant features give no parallax, scale drifts, and the run diverges (a
known failure for feature SLAM).
EuRoC (stereo)
seq
ATE RMSE (m)
ATE mean (m)
ATE max (m)
RPE RMSE (m)
fps
MH_01_easy
0.059
0.048
0.225
0.910
145
MH_02_easy
0.067
0.062
0.133
0.848
87
MH_03_medium
0.104
0.092
0.257
1.155
23
MH_04_difficult
0.253
0.243
0.416
1.187
44
MH_05_difficult
0.090
0.070
0.608
1.126
158
V1_01_easy
0.102
0.094
0.184
0.913
182
V1_02_medium
0.068
0.062
0.161
0.998
158
V1_03_difficult
0.126
0.103
0.585
0.890
173
V2_01_easy
0.093
0.079
0.300
0.697
162
V2_02_medium
0.129
0.110
0.405
0.892
122
V2_03_difficult
diverged
186
TUM RGB-D
Standard
seq
ATE RMSE (m)
ATE mean (m)
ATE max (m)
RPE RMSE (m)
fps
fr1_desk
0.079
0.067
0.171
0.068
64
fr2_xyz
0.013
0.012
0.045
0.025
141
fr3_long_office_household
0.044
0.041
0.104
0.027
179
Dynamic (moving people)
Top row walking, bottom row sitting.
seq
ATE RMSE (m)
ATE mean (m)
ATE max (m)
RPE RMSE (m)
fps
fr3_sitting_static
0.030
0.022
0.080
0.017
119
fr3_sitting_xyz
0.060
0.053
0.171
0.074
112
fr3_sitting_rpy
0.070
0.042
0.381
0.096
111
fr3_sitting_halfsphere
0.088
0.066
0.308
0.128
107
fr3_walking_static
0.266
0.183
0.893
0.494
87
fr3_walking_halfsphere
0.436
0.360
1.232
0.521
91
fr3_walking_xyz
0.525
0.374
1.492
0.494
77
fr3_walking_rpy
0.682
0.570
1.337
0.550
88
Low-motion (sitting) sequences hold; high-motion (walking) sequences degrade, as for any feature
SLAM without explicit dynamic-object handling.
TartanAir (stereo)
seq
ATE RMSE (m)
ATE mean (m)
RPE RMSE (m)
fps
P000
0.225
0.185
1.425
165
P001
0.575
0.551
1.300
154
P002
0.217
0.133
1.524
147
P003
0.068
0.061
1.698
145
P004
0.098
0.075
1.698
141
P005
0.605
0.436
1.247
139
P006
0.813
0.637
1.937
157
The KITTI segment metric (t_rel) is undefined here: TartanAir trajectories are too short for the
100-800 m segments.
TartanGround (stereo)
seq
ATE RMSE (m)
ATE mean (m)
RPE RMSE (m)
t_rel (%)
fps
P0000
2.582
1.934
1.505
20.58
98
P0001
0.290
0.238
1.408
22.36
115
P0002
1.346
1.134
1.484
24.82
109
P0003
0.400
0.254
1.444
17.73
127
P0004
0.969
0.821
1.552
16.98
96
Quick start
Install the command-line tools (Ubuntu):
sudo apt install git make docker.io nvidia-container-toolkit gh
curl -LsSf https://astral.sh/uv/install.sh | sh # uv, the Python tool runner
Then, from a fresh clone on a machine with an NVIDIA GPU:
make build-image-tensorrt10 # container (Ubuntu 24.04, TensorRT 10)
uv run python scripts/models/download_onnx_engine_superpoint.py # prebuilt ONNX models (~90 MB)
uv run python scripts/models/download_onnx_engine_lightglue.py
uv run python scripts/models/download_onnx_engine_eigenplaces.py
make build-engines-tensorrt10 # TensorRT engines for this GPU
make build-superslam # compile SuperSLAM
uv run python scripts/datasets/download_kitti.py --out ~/datasets/kitti
make run-superslam-kitti # writes results/kitti/00.txt
make evaluate-superslam-kitti # ATE and RPE
make help lists every target. Every Python step runs through uv, which builds a throwaway
environment, so nothing is installed globally.
conda or pipx, Docker Compose version, bare-metal dependencies
Docker Compose v2 2.30 or newer is required for the gpus: key in compose.yaml. Check with
docker compose version. On an older Compose, edit compose.yaml: comment out gpus: all and
uncomment the deploy: block.
A bare-metal build (no Docker) also needs CUDA 12.9, TensorRT 10.11, cuDNN 9, and the C++ libraries.
One script installs them:
bash scripts/setup/install_dependencies.sh # Ubuntu 22.04 or 24.04
Usage
# Datasets, downloaded into ~/datasets/<name>
uv run python scripts/datasets/download_kitti.py --out ~/datasets/kitti
uv run python scripts/datasets/download_euroc.py --out ~/datasets/euroc --area machine_hall
uv run python scripts/datasets/download_tum.py --out ~/datasets/tum --seq fr2_xyz
# Run, writing the trajectory into results/<dataset>/
make run-superslam-kitti SEQUENCE=00
make run-superslam-euroc EUROC=MH_01_easy
make run-superslam-tum TUM=rgbd_dataset_freiburg2_xyz
# Evaluate: ATE and RPE
make evaluate-superslam-kitti
make evaluate-superslam-euroc
make evaluate-superslam-tum
Per-dataset tuning knobs (window size, loop thresholds, and others) live in the dataset YAML under
examples, in the Backend, Tracking, and loop sections. Each knob also reads a SUPERSLAM_*
environment variable; precedence is the environment variable, then the YAML value, then the default.
The GTSAM optimization core builds and tests without a GPU:
make test-superslam
TensorRT 10 or 11, other GPUs, bare-metal build, cleaning
TensorRT 10 or 11. TensorRT 10 is the default and tested path. For 11:
make build-image-tensorrt11
make build-engines-tensorrt11
An engine built for one TensorRT version does not load under the other, so rebuild the engines after
switching. TensorRT 10 or newer is required for LightGlue (opset-18 ONNX).
Other GPUs. The code targets Ampere (sm_86), Ada (sm_89), and Blackwell (sm_120). To move to a
different NVIDIA GPU, rebuild the engines (they are GPU-architecture specific):
make build-engines-tensorrt10 && make build-superslam
Cleaning.make clean-engines, clean-weights, clean-build, clean-results, clean-images,
or clean-all. Every target is safe to re-run.
The .onnx.data sidecar, and publishing models
.onnx.data. Some models ship as a pair: model.onnx holds the graph, and model.onnx.data
holds the large weight tensors. LightGlue and EigenPlaces are stored this way, so both files must
sit in weights/ together. The download scripts fetch the sidecar automatically. The SuperPoint
ONNX has inline weights and no .data file.
Publishing the prebuilt ONNX (maintainers). The download scripts pull from a GitHub Release.
Name each release weights-<YYYYMMDDHHMMSS> and point the downloaders at it by setting DEFAULT_TAG
in scripts/models/_release.py:
评论