D-SLAM is a Visual SLAM system using monocular RGB inputs and estimated depth inputs to construct a 3D mapping. The goal of this project is to create a well-performing SLAM system under contrained resources setup while aiming for real-time performance. The D-SLAM system can supports stream of RGB images either from disk or camera.
D-SLAM is based on MonoDepth2 depth estimator and ORB-SLAM2 system, and it be trained and evaluated with monocular RGB image stream without collecting additional image stream, or depth information from sensor.
D-SLAM is evaluated on the KITTI dataset. We have also run a real-world experiment with NVIDIA Jetson TX2 showing that D-SLAM was able to detect loops and relocalize camera in real time.
You can find all the details about installation and results at our Project Website
- Chu-Hung Cheng
- Henrry Gunawan
- Kartik Patwari
- Zhiwei Zhang
If you have any questions, please contact Chu-Hung Cheng