1.2 有哪些常见的视觉 + IMU 融合方案？有没有工业界应用的例子？
1). 常见的视觉 + IMU融合方案
a. 优化方法: VINS, OKVIS, ORB + IMU, SVO+GTSAM
b. 滤波方法: rovio, msf, msckf, SVO+MSF,
2). 工业解应用的例子
谷歌: Tango(目前已停产),ARCore
微软: HoloLens
苹果: ARKit
1.3 在学术界， VIO 研究有哪些新进展？有没有将学习方法用到 VIO中的例子？
1). VIO研究有哪些新进展？
a. GPS + VIO[ 1 ] ^{[1]}[1]: GPS信息和VIO的紧耦合，适用于长航时
b. GPU前端加速的VIO[ 2 ] ^{[2]}[2]: 利用GPU加速前端算法，Faster than FAST
c. 结合线段特征: PL-VIO[ 3 ] ^{[3]}[3], Trifo-VIO[ 4 ] ^{[4]}[4]
d. 激光SLAM融合: V-LOAM[ 5 ] ^{[5]}[5], VISO2 + LOAM[ 6 ] ^{[6]}[6]
等等…
[1]. Cioffi, Giovanni, and Davide Scaramuzza. “Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry.” arXiv preprint arXiv:2003.04159 (2020).
[2]. Nagy, Balázs, Philipp Foehn, and Davide Scaramuzza. “Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO.” arXiv preprint arXiv:2003.13493 (2020).
[3]. YiJia, He, et al. “PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features.” (2018).
[4]. Zheng, Feng, et al. “Trifo-VIO: Robust and efficient stereo visual inertial odometry using points and lines.” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018.
[5]. Zhang, Ji, and Sanjiv Singh. “Visual-lidar odometry and mapping: Low-drift, robust, and fast.” 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015.
[6]. Yan, Min, et al. “Loose coupling visual-lidar odometry by combining VISO2 and LOAM.” 2017 36th Chinese Control Conference (CCC). IEEE, 2017.
2). 有没有将学习方法用到 VIO中的例子？
a. Vinet[ 1 ] ^{[1]}[1]
b. DeepVIO[ 2 ] ^{[2]}[2]
b. 半监督，无监督学习的VIO[ 3 ] ^{[3]}[3]
[1].Clark, Ronald, et al. “Vinet: Visual-inertial odometry as a sequence-to-sequence learning problem.” Thirty-First AAAI Conference on Artificial Intelligence. 2017.
[2]. Han, Liming, et al. “DeepVIO: Self-supervised deep learning of monocular visual inertial odometry using 3D geometric constraints.” arXiv preprint arXiv:1906.11435 (2019).
[3]. Tian, Yuan, and Marc Compere. “A Case Study on Visual-Inertial Odometry using Supervised, Semi-Supervised and Unsupervised Learning Methods.” 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). IEEE, 2019.
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