27. 27
[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
28. [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))
28
36. [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
36
37. [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
37
41. 参考文献 (カメラによるVisual SLAM)
[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:
DenseTracking and Mapping in Real-Time. In International Conference on Computer
Vision.
[Engel2014]Engel, J., Schops,T., & Cremers, D. (2014). LSD-SLAM: Large-Scale Direct
monocular SLAM. In European Conference on ComputerVision
[Mur-Artal2015]Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015). ORB-SLAM:A
Versatile 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 : DeepTracking and
Mapping. In European Conference on ComputerVision.
41
58. [Whelan2016]ElasticFusion (4/4)
TUM RGB-D DatasetでLocalization評価
ICL-NUIM DatasetでMapping評価
Surfelと処理速度の関係
Intel Core i7-4930K CPU at 3.4GHz,
32GB of RAM
nVidia GeForce GTX 780 Ti GPU
with 3GB mem
58
62. [Dai2017]BundleFusion (4/4)
Structure Sensorで取得した屋内データでの比較 ICL-NUIM DatasetでMapping評価
TUM RGB-D DatasetでLocalization評価 パフォーマンス評価
Core i7 3.4GHz CPU (32GB RAM)
NVIDIA GeForce GTXTitan X (for reconstruction)
NVIDIA GTXTitan Black (for search / global pose optimization)
62
63. 参考文献 (RGB-D SLAM)
[Newcombe2011]Newcombe, R. a., Davison,A. J., Izadi, S., Kohli,
P., Hilliges, O., Shotton, J., … Fitzgibbon,A. (2011). KinectFusion:
Real-time dense surface mapping and tracking. IEEE International
Symposium on Mixed and Augmented Reality.
[Kerl2013]Kerl, C., Strum, J., & Cremers, D. (2013). DenseVisual
SLAM for RGB-D Cameras. In IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS).
[Whelan2016]Whelan,T., Salas-Moreno, R. F., Glocker, B.,
Davidson,A. J., & Leutenegger, S. (2016). ElasticFuion: Real-Time
Dense SLAM and Light Source Estimation. The International
Journal of Robotics Research.
[Dai2017]Dai,A., Niessner, M., Zollhofer, M., Izadi, S., & Theobalt,
C. (2017). BundleFusion: Real-time Globally Consistent 3D
Reconstruction using On-the-fly Surface Re-integration. ACM
Transactions on Graphics (TOG).
63
81. 参考文献 (Visual Inertial SLAM)
[Leutenegeer2015]Leutenegeer, S., Furgale, P., Rabaud,V., Chli,
M., Konolige, K., & Siegwart, R. (2015). Keyframe-BasedVisual-
Inertial SLAM Using Nonlinear Optimization. The International
Journal of Robotics Research, (september).
[Qin2018]Qin,T., Li, P., & Shen, S. (2018).VINS-Mono:A Robust
andVersatile MonocularVisual-Inertial State Estimator. IEEE
Transactions on Robotics, 34(4), 1004–1020.
[Bloesch2017]Bloesch, M., Burri, M., Omari, S., Hutter, M., &
Siegwart, R. (2017). IEKF-basedVisual-Inertial Odometry using
Direct Photometric Feedback. The International Journal of
Robotics Research, 36(1053–1072).
[Schnider2017]Schnider,T., Dymczyk, M., Fehr, M., Egger, K.,
Lynen, S., Gilitschenski, I., & Siegwart, R. (2017). maplab:An
Open Framework for Research inVisual-inertial Mapping and
Localization. IEEE Robot, 3, 1418–1425.
81