PRBonn/LiDAR-MOS
(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
This project helps self-driving car engineers and robotics researchers accurately distinguish between moving and static objects using 3D LiDAR data. It takes raw 3D LiDAR scans as input and outputs a segmented scene, clearly marking dynamic elements like moving cars versus static ones like parked cars. The end-user is typically an engineer or researcher focused on improving the perception systems of autonomous vehicles or mobile robots.
678 stars. No commits in the last 6 months.
Use this if you need to reliably identify and separate moving objects from stationary ones in real-time 3D LiDAR data for applications like autonomous navigation or robotics.
Not ideal if your primary need is for semantic segmentation (identifying 'car', 'pedestrian', 'tree') rather than specifically distinguishing between moving and static states.
Stars
678
Forks
110
Language
Python
License
MIT
Category
Last pushed
Dec 21, 2022
Commits (30d)
0
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