A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors

By Tong Qin, Shaozu Cao, Jie Pan, Shaojie Shen. arXiv.org, 2019

446 citations recorded by the source corpus.

Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8c11a5a7ed0fb4c9e77b955894e681fbddcd9aae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

  1. Shaozu Cao
  2. Xiuyuan Lu
  3. Shaojie Shen
  4. Davide Scaramuzza

In Computer Vision and Pattern Recognition

  1. Shaozu Cao
  2. Xiuyuan Lu
  3. Shaojie Shen
  4. Davide Scaramuzza

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