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For input, M-detector accepts either an individual point or a frame of
points, with the sensor ego-motion compensated in advance. The result of ego-
motion compensation can be provided by a LiDAR-based odometry or a full
Simultaneous localization and mapping (SLAM) system.
If that is the case, won't the ego-motion estimate from LiDAR-based odometry/SLAM be untrustworthy due to dynamic objects in the raw pointcloud data?
What's the impact of additional uncertainity in position estimate from LiDAR-based odometry/SLAM due to dynamic events on M-detector's performance?
Can we use M-detector without prior ego motion information(e.g. from FAST_LIO)? Or, Use it as a preprocessing module which removes dynamic events from the pointcloud given to LiDAR-based odometry/SLAM?
Is robust localization also a use case? Or, mapping only?
The text was updated successfully, but these errors were encountered:
Hi, @HuajieWu99 @XW-HKU @Ecstasy-EC. Kudos for the great work.
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The text was updated successfully, but these errors were encountered: