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This is a python implementation of RGBD-PTAM, the code is modified fromstereo_ptam, which is based on C++ projectlrse/sptam and paper "S-PTAM: Stereo Parallel Tracking and Mapping" Taihu Pire et al. RAS17.

RGBD-PTAM is a RGBD SLAM system able to compute the camera trajectory in real-time. It heavily exploits the parallel nature of the SLAM problem, separating the time-constrained pose estimation from less pressing matters such as map building and refinement tasks. On the other hand, the RGBD setting allows to reconstruct a metric 3D map for each frame, improving the accuracy of the mapping process with respect to monocular SLAM and avoiding the well-known bootstrapping problem. Also, the real scale of the environment is an essential feature for robots which have to interact with their surrounding workspace.

RGB-D system has direct depth measurements, by setting a pasudo stereo baseline, disparity can be computed from depth, then stereo measurements can be synthetized. Now the problem is converted to stereo SLAM, we can directly reuse S-PTAM's solution andstereo_ptam's code. Below is S-PTAM's system overview (fromS-PTAM paper page 11):

Because one RGB-D frame has only one image, the computation burden is smaller than stereo setting, actually this project is faster thanstereo_ptam, reach 30~50ms per frame (depending on keyframes adding frequency).

• Multithreads Tracking, Mapping, and Loop Closing
• Covisibility Graph
• Local Bundle Adjustment
• Pose Graph Optimization
• Motion Model
• Visualization
• Data loader for datasetsTUM RGB-D andICL-NUIM RGB-D
• Relocalization (tracking failure recovery)
• Dense point clouds visualization
• Exhaustive evaluation

• Python 3.6+
• numpy
• cv2
• g2o_{(python binding of C++ libraryg2o)} for optimization
• pangolin_{(python binding of C++ libraryPangolin)} for visualization

python ptam.py --dataset tum --path path/to/your/TUM_RGBD_dataset/rgbd_dataset_freiburg1_room
or
python ptam.py --dataset icl --path path/to/your/ICL-NUIM_RGBD_dataset/living_room_traj3_frei_png

Visual results on TUM-RGBD dataset sequence "rgbd_dataset_freiburg1_room":

• graph:
  
• point cloud (sparse):
  

Followingstereo_ptam, this project is released under GPLv3 License.

If you have problems related to the base S-PTAM algorithm, you can contact original authorslrse (robotica@dc.uba.ar), or refer to the paper:
[1] Taihú Pire,Thomas Fischer, Gastón Castro, Pablo De Cristóforis, Javier Civera and Julio Jacobo Berlles.S-PTAM: Stereo Parallel Tracking and MappingRobotics and Autonomous Systems, 2017.

If you have interest in this python implementation, email me (Hang Qi,qihang@outlook.com);