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dom-lee committed Apr 20, 2023
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Expand Up @@ -7,8 +7,8 @@ ROB 530 Mobile Robotics Final Project - University of Michigan - Ann Arbor
## Abstract
Simultaneous Localization and Mapping (SLAM) is not robust in dynamic environments, where objects move and cause changes in the surroundings, its effectiveness is limited. It is particularly challenging to use SAM methods that depend on visual or point cloud data, as the dynamic objects can be registered in multiple scans. To address this issue, we propose a method that combines Sparsely Embedded Convolutional Detection (SECOND) and LiDAR Inertial Odometry Smoothing And Mapping. Our approach involves masking out the point cloud features detected by SECOND in the Velodyne scan. Then we occlude the features and update it to LIO-SAM to help it develop the map and state estimation. Our experimental results demonstrate the effectiveness of our approach, which achieves good? accuracy in localization compared to the original LIO-SAM method. By enabling SLAM to work more effectively in dynamic environments, this could enhance the safety and reliability of various autonomous systems.

![overview](https://github.com/dom-lee/Masked-LIO-SAM/doc/masked-lio-sam_overview.gif)
![overview](https://github.com/dom-lee/Masked-LIO-SAM/doc/masked-lio-sam_birdeye.gif)
![overview](https://github.com/dom-lee/Masked-LIO-SAM/tree/master/doc/masked-lio-sam_overview.gif)
![birdeye](https://github.com/dom-lee/Masked-LIO-SAM/tree/master/doc/masked-lio-sam_birdeye.gif)



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