24. Visual SLAMの研究例
[Uchiyama2015] Uchiyama, H.,Taketomi,T., Ikeda, S., & Monte
Lima, J. P. S., "AbecedaryTracking and Mapping: a Toolkit for
Tracking Competitions," Proceedings of the 14th IEEE
International Symposium on Mixed and Augmented Reality,
pp.198-199, 2015.
[Klein2007]Klein, G., & Murray, D. (2007). ParallelTracking and
Mapping for Small AR Workspaces. In IEEE and ACM
International Symposium on Mixed and Augmented Reality, ISMAR.
[Newcombe2011]Newcombe, R.A., Lovegrove, S. J., & Davison,
A. J. (2011). DTAM: Dense Tracking and Mapping in Real-Time.
In International Conference on ComputerVision.
[Engel2014]Engel, J., Schops,T., & Cremers, D. (2014). LSD-
SLAM: Large-Scale Direct monocular SLAM. In European
Conference on ComputerVision
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25. Visual SLAMの研究例
[Mur-Artal2015]Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D.
(2015). ORB-SLAM:AVersatile and Accurate Monocular SLAM
System. IEEETransactions on Robotics, 31(5), 1147–1163.
[Mur-Artal2016]Mur-Artal, R., &Tardos, J. D. (2016). ORB-
SLAM2: an Open-Source SLAM System for Monocular, Stereo
and RGB-D Cameras. ArXiv, (October). Retrieved from
[Tateno2017]Tateno, K.,Tombari, F., Laina, I., & Navab, N. (2017).
CNN-SLAM : Real-time dense monocular SLAM with learned
depth prediction. In IEEE Conference on ComputerVision and
Pattern Recognition.
[Zhou2018]Zhou, H., & Ummenhofer, B. (2018). DeepTAM :
Deep Tracking and Mapping. In European Conference on
ComputerVision.
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34. 34
[Engel2014]LSD-SLAM (2/3)
Tracking
濃度勾配の高い画素のみPose推定に使用(Semi-Dense)
深度を使ってKeyFrameの画素を現フレームに投影し、差分を最小
化するようPose推定 (Direct法)
Depth Map Estimation
Poseの変化が閾値を超えたらKeyFrame生成
KeyFrameの深度初期値を前KeyFrameの深度を投影して生成
追跡フレームとKeyFrameとのベースラインステレオで深度を補正*
Map Optimization
KeyFrame生成時近傍のKeyFrameおよび類似KeyFrameを取得し、
それぞれLoopかを判別
Loopが存在する場合、2つのKeyFrameの画素と深度から相対Pose
を求め、それをLoop上を伝播させて最適化(Graph Optimization)
*J. Engel, J. Sturm, and D. Cremers. Semi-dense visual odometry for a monocular camera. In IEEE International Conference
on ComputerVision (ICCV), December 2013
35. [Engel2014]LSD-SLAM (3/3)
[9]Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera.
In: Intl. Conf. on ComputerVision (ICCV) (2013)
[15]Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Intl.
Symp. on Mixed and Augmented Reality (ISMAR) (2007)
[14]Kerl, C., Sturm, J., Cremers, D.: Dense visual SLAM for RGB-D cameras. In: Intl.
Conf. on Intelligent Robot Systems (IROS) (2013)
[7]Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard,W.:An evaluation of
the RGB-D slam system. In: Intl. Conf. on Robotics and Automation (ICRA) (2012)
TUM-RGBDベンチマーク(軌跡の二乗誤差(cm))
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43. [Tateno2017]CNN-SLAM (2/3)
Camera Pose Estimation
現フレームの画素を前キーフレーム上へ投影した時の差が最
小となるPoseを推定(Direct法)
LSD-SLAM同様、輝度勾配の高い領域
投影時にCNNで推定した深度情報を使用
LSD-SLAMではKey-Frame間のステレオで深度推定
CNN Depth Prediction & Semantic Segmentation
Laina, I., Rupprecht, C., Belagiannis,V.,Tombari, F., & Navab, N.
(2016). Deeper Depth Prediction with Fully Convolutional
Residual Networks. IEEE International Conference on 3DVision.
各KeyFrameに対し深度推定
LSD-SLAMと同様にbaseline stereoを用いて深度を補正
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44. [Tateno2017]CNN-SLAM (3/3)
ICL-NUIM datasetとTUM datasetによる軌跡と深度の精度評価
以下の環境でリアルタイム
• Intel Xeon CPU at 2.4GHz with 16GB of RAM
• Nvidia Quadro K5200 GPU with 8GB of VRAM
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