Non-iterative SLAM

An efficient RGB-D-inertial Dense Mapping with Closed-form Solution

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In this work, we propose an efficient RGB-D-inertial dense mapping method with a closed-form solution. The paper titled “Non-iterative SLAM” received the best paper award in robotic planning from the International Conference on Advanced Robotics (ICAR).

Real-time dense mapping on a laptop.

Existing SLAM methods often require iterative solutions to find data association. For example, they require gradient descent algorithm to perform bundle adjustment, require RANSAC and ICP to match two point clouds. However, all of them are computational expensive or sensitive to initialization.

To solve this problem, we develop Non-iterative SLAM, which has a non-iterative solution. It is the first RGB-D-Inertial SLAM method that has a closed-from solution only with a complexity of O(N log N).

Because of this, it is very computationally efficient. We can even perform real-time dense mapping on a credit card sized computing board.

Live demo on a credit card sized computing board. No post-processing required.

To the best of our knowledge, it was the world’s first real-time dense mapping demo on such a small computer. It can provide centimeter level accuracy with an onboard low power processor on a flying robot.

Real-time trajectory estimation on a flying robot.

The closed-form solution is based on our kernel cross-correlator (KCC), which is published in AAAI 2017. KCC is robust to noises, thus the trajectory estimation is also smoother than other methods.

Robot Localization only with Ground Textures.
The QR code is for calculating the drift error instead of localization.

We also applied it to warehouse robots for high-precision localization only with ground textures. In the tests, it only produces 0.1% to 0.5% drift error if no loop closure is given.

Theoretically, the closed-form solution is based on our paper titled “Kernel Cross-Correlator”, which has a closed-form solution in frequency domain. For more detailed information, please refer to our papers.

Paper: Non-iterative SLAM

Paper: Non-iterative RGB-D-inertial Odometry

Paper: Kernel Cross-Correlator

Citation

@inproceedings{wang2017non,
 title={Non-iterative SLAM},
 author={Wang, Chen and Yuan, Junsong and Xie, Lihua},
 booktitle={International Conference on Advanced Robotics (ICAR)},
 pages={83--90},
 year={2017},
 organization={IEEE},
}

@article{wang2017noniterative,
 title={Non-iterative RGB-D-inertial Odometry},
 author={Wang, Chen and Hoang, Minh-Chung and Xie, Lihua and Yuan, Junsong},
 journal={arXiv preprint arXiv:1710.05502},
 year={2017},
}

@inproceedings{wang2018kernel,
 title={Kernel Cross-Correlator},
 author={Wang, Chen and Zhang, Le and Xie, Lihua and Yuan, Junsong},
 booktitle={Thirty-Second AAAI Conference on Artificial Intelligence (AAAI)},
 pages={4179--4186},
 year={2018},
}