التفاصيل البيبلوغرافية
| العنوان: |
Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments |
| المؤلفون: |
Zhao, Shibo, Zhang, Hengrui, Wang, Peng, Nogueira, Lucas, Scherer, Sebastian |
| سنة النشر: |
2021 |
| المجموعة: |
ArXiv.org (Cornell University Library) |
| مصطلحات موضوعية: |
Computer Science - Robotics |
| الوصف: |
We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10 % of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won $1^{st}$ and $2^{nd}$ place in the Tunnel and Urban Circuits, respectively. |
| نوع الوثيقة: |
text |
| اللغة: |
unknown |
| العلاقة: |
http://arxiv.org/abs/2104.14938Test |
| الإتاحة: |
http://arxiv.org/abs/2104.14938Test |
| رقم الانضمام: |
edsbas.AC556333 |
| قاعدة البيانات: |
BASE |
