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Paper ThAP.69

Ling, Yonggen (The Hong Kong University of Science and Technology), Shen, Shaojie (Hong Kong University of Science and Technology)

A Dense Visual-Inertial Fusion Approach for Tracking of Aggressive Motions

Scheduled for presentation during the Poster session "Late Breaking Posters" (ThAP), Thursday, October 1, 2015, 09:45−10:00, Saal G1

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 20, 2019

Keywords SLAM, Sensor Fusion, Localization

Abstract

We propose a sliding window-based dense visual-inertial fusion method for real-time tracking of challenging aggressive motions. Our method combines recent advances in direct dense visual odometry, IMU preintegration, and graph-based optimization. At the front-end, a direct dense visual odometry provides camera pose tracking that is resistant to motion blur. At the back-end, a sliding window optimization-based fusion framework with efficient IMU-preintegration generates smooth and high-accuracy state estimates even with occasional visual tracking failures. A local loop closure that is integrated into the back-end further eliminates drifts after extremely aggressive motions. Our system runs real-time at 25Hz on an off-the-shelf laptop. Experimental results shows that our method is able to accurately track motions with angular velocities up to 1000 degree/s and velocities up to 4 m/s. We also compare our method with state-of-the-art systems such as Google Tango and show superior performance during challenging motions. We show that our method achieves reliable tracking results even if we throw the sensor suite during experiments.

 

 

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