SGBA: semantic gaussian mixture model-based LiDAR bundle adjustment
LiDAR bundle adjustment (BA) is an effective approach to reduce the drifts in pose estimation from the front-end. Existing works on LiDAR BA usually rely on predefined geometric features for landmark representation. This reliance restricts generalizability, as the system will inevitably deteriorate...
Main Authors: | Ji, Xingyu, Yuan, Shenghai, Li, Jianping, Yin, Pengyu, Cao, Haozhi, Xie, Lihua |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2025
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/182120 |
Similar Items
-
LIO-GVM: an accurate, tightly-coupled Lidar-inertial odometry with Gaussian voxel map
by: Ji, Xingyu, et al.
Published: (2024) -
SE-Calib: semantic edge-based LiDAR-camera boresight online calibration in urban scenes
by: Liao, Youqi, et al.
Published: (2023) -
Mechanical properties of bundled carbon nanoscroll
by: Huang, Jie, et al.
Published: (2018) -
HCTO: optimality-aware LiDAR inertial odometry with hybrid continuous time optimization for compact wearable mapping system
by: Li, Jianping, et al.
Published: (2024) -
Correlates of University Adjustment Among Malaysian Students
by: Usmani, Aisha
Published: (2002)