Problem

Our team was tasked with developing a system to improve visual SLAM amidst dynamic occlusions. The challenge was that traditional SLAM systems struggle to accurately track the camera's position and orientation in environments where objects are moving, leading to errors in mapping and localization.

To solve this problem, we needed to develop a system that could detect and track moving objects in real-time while also updating the map accurately.

Click here to see our solution in action.

Solution

We developed a Dynamic Occlusion-Aware Monocular SLAM system that utilized instance segmentation, optical flow, and video inpainting. Our system first detects and segments out moving objects using instance segmentation. Then, we use optical flow to estimate the motion of these objects over time and update the map accordingly.

To fill in the gaps left by occluded areas, we used video inpainting techniques to generate plausible predictions of what would be visible if there were no occlusions present. This allowed us to maintain accurate tracking even when parts of the scene were temporarily obscured.

Impact

Improved accuracy of visual SLAM by X%
Reduced errors caused by dynamic occlusions by X%

"Our Dynamic Occlusion-Aware Monocular SLAM system has had an incredible impact on improving visual SLAM accuracy amidst dynamic occlusions."

Team

Team Member #1 - Computer Vision Engineer
- Developed instance segmentation algorithm for detecting moving objects
- Implemented optical flow-based motion estimation and map updating
Team Member #2 - Machine Learning Engineer
- Developed video inpainting techniques for filling in occluded areas of the scene
- Optimized system performance through parallelization and hardware acceleration

Dynamic Occlusion-Aware Monocular SLAM System