![Unsupervised Collaborative Learning of
Keyframe Detection and Visual Odometry
Towards Monocular Deep SLAM [Sheng & Xu+, ICCV’19]
東京大学 相澤研究室
M2 金子 真也](https://image.slidesharecdn.com/kfsfmlearner-191212084244/85/Unsupervised-Collaborative-Learning-of-Keyframe-Detection-and-Visual-Odometry-Towards-Monocular-Deep-SLAM-1-320.jpg)
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Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAMの解説
- 1. Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM [Sheng & Xu+, ICCV’19] 東京大学 相澤研究室 M2 金子 真也
- 2. 1 本論文 • Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM – 著者: L. Sheng, D. Xu, W. Ouyang and X. Wang – 所属: Beihang University, Oxford, SenseTime – 採択会議: ICCV2019
- 3. 2 本論文 • Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM – 著者: L. Sheng, D. Xu, W. Ouyang and X. Wang – 所属: Beihang University, Oxford, SenseTime – 採択会議: ICCV2019 – Monocular Deep SLAMを実現したいという強い気持ちの論文 – わかりみが深い
- 4. 3 Introduction • Visual SLAM – 3D reconstruction + Camera pose estimation – 両者の同時最適化 (Bundle Adjustment) Direct Sparse Odometry [Engel+, TPAMI’18]
- 5. 4 Introduction • Deep Learning for Visual SLAM (Deep SLAM) End-to-end Deep SLAMDL helps SLAM SfMLearner [Zhou+, CVPR’17] CNN-SLAM [Tateno+, CVPR’17] CodeSLAM [Tateno+, CVPR’18] DeepTAM [Zhou+, ECCV’18] This figure is from Tombari’s presentation slide @ ICCVW.
- 6. 5 Introduction • Deep Learning for Visual SLAM (Deep SLAM) End-to-end Deep SLAMDL helps SLAM CNN-SLAM [Tateno+, CVPR’17] CodeSLAM [Tateno+, CVPR’18] DeepTAM [Zhou+, ECCV’18] SfMLearner [Zhou+, CVPR’17] 本論文の目標は, この領域での最高のDeep SLAMを作ること This figure is from Tombari’s presentation slide @ ICCVW.
- 7. 6 Related works • SfMLearner [Zhou+, CVPR’17] – 古典的なSfMを応用し, UnsupervisedにDeep SLAMを実現 – Training • Photometric errorを最小化するように学習
- 8. 7 Related works • SfMLearner [Zhou+, CVPR’17] – 古典的なSfMを応用し, UnsupervisedにDeep SLAMを実現 – Inference • 入力画像の奥行き画像と, ２視点間のカメラ姿勢をCNNで回帰
- 9. 8 Related works • SfMLearner [Zhou+, CVPR’17] – 古典的なSfMを応用し, UnsupervisedにDeep SLAMを実現 – Inference • 入力画像の奥行き画像と, ２視点間のカメラ姿勢をCNNで回帰 より従来のVSLAMに近い Deep SLAMを実現するためには？？？
- 10. 9 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA)
- 11. 10 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) 画像 𝑍𝑍𝑗𝑗 画像 𝑍𝑍𝑗𝑗+1 𝒖𝒖𝑖𝑖,𝑗𝑗+1特徴点 𝒖𝒖𝑖𝑖,𝑗𝑗 カメラ姿勢 [𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗] 𝒖𝒖𝑖𝑖,𝑗𝑗 投影点 𝑓𝑓 𝐗𝐗𝑖𝑖 𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗) Bundle 𝑍𝑍𝑗𝑗+1 [𝐑𝐑𝑗𝑗+1, 𝐭𝐭𝑗𝑗+1] 𝒖𝒖𝑖𝑖,𝑗𝑗+1 画像 𝑍𝑍𝑗𝑗 3D位置 𝐗𝐗𝑖𝑖
- 12. 11 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) 画像 𝑍𝑍𝑗𝑗 画像 𝑍𝑍𝑗𝑗+1 𝒖𝒖𝑖𝑖,𝑗𝑗+1特徴点 𝒖𝒖𝑖𝑖,𝑗𝑗 最適化 画像 𝑍𝑍𝑗𝑗 𝒖𝒖𝑖𝑖,𝑗𝑗 Bundle [𝐑𝐑𝑗𝑗+1, 𝐭𝐭𝑗𝑗+1] 𝑍𝑍𝑗𝑗+1 𝒖𝒖𝑖𝑖,𝑗𝑗+1 3D位置 𝐗𝐗𝑖𝑖 カメラ姿勢 [𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗] 投影点 𝑓𝑓 𝐗𝐗𝑖𝑖 𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗)
- 13. 12 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ 画像 𝑍𝑍𝑗𝑗 画像 𝑍𝑍𝑗𝑗+1 𝒖𝒖𝑖𝑖,𝑗𝑗+1特徴点 𝒖𝒖𝑖𝑖,𝑗𝑗 最適化 画像 𝑍𝑍𝑗𝑗 𝒖𝒖𝑖𝑖,𝑗𝑗 Bundle [𝐑𝐑𝑗𝑗+1, 𝐭𝐭𝑗𝑗+1] 𝑍𝑍𝑗𝑗+1 𝒖𝒖𝑖𝑖,𝑗𝑗+1 3D位置 𝐗𝐗𝑖𝑖 カメラ姿勢 [𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗] 投影点 𝑓𝑓 𝐗𝐗𝑖𝑖 𝐑𝐑𝑗𝑗, 𝐭𝐭𝑗𝑗) Keyframe
- 14. 13 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ Keyframe [1] ORB-SLAM2 for Monocular, Stereo and RGB-D Cameras [Mur-Artal+, ToR17]
- 15. 14 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ – Keyframeの選び方 1. 重複を避けるようにある程度間隔を空ける
- 16. 15 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ – Keyframeの選び方 1. 重複を避けるようにある程度間隔を空ける 2. 十分な地図が作れなくなるのでそれなりに必要
- 17. 16 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ – Keyframeの選び方 1. 重複を避けるようにある程度間隔を空ける 2. 十分な地図が作れなくなるのでそれなりに必要 → 職人技のような挿入条件の設定が必要
- 18. 17 Related works • VSLAM – VSLAMで最も重要な要素はBundle Adjustment (BA) • 三次元地図に, 三次元点とその点が属するカメラ画像を登録 • カメラ画像をKeyframe (KF)と呼ぶ – Keyframeの選び方 1. 重複を避けるようにある程度間隔を空ける 2. 十分な地図が作れなくなるのでそれなりに必要 → 職人技のような挿入条件の設定が必要 – この選択をCNNで実現し, SfMLearnerに組み込めないか？
- 19. 18 Proposed method • SfMLearner with KF selection – KF選択を行いながら, 三次元復元とカメラ姿勢推定を行うような Deep SLAMの実現
- 20. 19 Proposed method • SfMLearner with KF selection – Unsupervised collaborative learning • 三次元復元 + カメラ姿勢推定 + KF選択 ←New!!! KF selection network Depth + Camera pose network (Visual Odometry)
- 21. Depth + Camera pose network (Visual Odometry) 20 Proposed method • SfMLearner with KF selection – Unsupervised collaborative learning • 三次元復元 + カメラ姿勢推定 + KF選択 ←New!!! KF selection network - 2枚の画像間のsimilarity scoreを回帰 - このscoreに応じてKFの 選択を行う
- 22. 21 Proposed method • SfMLearner with KF selection – Unsupervised collaborative learning • 三次元復元 + カメラ姿勢推定 + KF選択 ←New!!! Depth + Camera pose network (Visual Odometry) KF selection network
- 23. 22 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Nearest Keyframe 2nd nearest Keyframe
- 24. 23 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} – Visual Odometry (≈SfMLearner) • Photometric error + cycle consistency + depth smooth term Target 𝐈𝐈𝑡𝑡 𝐃𝐃𝑡𝑡 Reference 𝐈𝐈𝑟𝑟 𝐃𝐃𝑟𝑟 Warped ref 𝐈𝐈𝑡𝑡←𝑟𝑟 𝐃𝐃𝑡𝑡 𝐃𝐃𝑟𝑟 Photometric error Cycle Consistency t r Target 𝐈𝐈𝑡𝑡Warped tgt 𝐈𝐈𝑟𝑟←𝑡𝑡 Warped2 tgt 𝐈𝐈𝑡𝑡←𝑟𝑟←𝑡𝑡 Warped tgt 𝐈𝐈𝑟𝑟←𝑡𝑡
- 25. 24 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} – Visual Odometry (≈SfMLearner) • Photometric error + cycle consistency + depth smooth term – Keyframe selection • Triplet loss <𝐈𝐈𝑡𝑡, 𝐈𝐈𝑠𝑠, 𝐈𝐈𝑝𝑝> s t p 0.1 n 大 小
- 26. 25 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} – Visual Odometry (≈SfMLearner) • Photometric error + cycle consistency + depth smooth term – Keyframe selection • Triplet loss <𝐈𝐈𝑡𝑡, 𝐈𝐈𝑠𝑠, 𝐈𝐈𝑝𝑝> <𝐈𝐈𝑡𝑡, 𝐈𝐈𝑠𝑠, 𝐈𝐈𝑛𝑛> s t p 0.1 n 大 小 大 小0.8
- 27. 26 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} – Visual Odometry (≈SfMLearner) • Photometric error + cycle consistency + depth smooth term – Keyframe selection • Triplet loss <𝐈𝐈𝑡𝑡, 𝐈𝐈𝑠𝑠, 𝐈𝐈𝑝𝑝> <𝐈𝐈𝑡𝑡, 𝐈𝐈𝑠𝑠, 𝐈𝐈𝑛𝑛> s t p 0.1 n 大 小 大 小0.8
- 28. 27 Proposed method • Training – Data pair • Sequential frames ℐ𝑠𝑠 ={𝐈𝐈𝑡𝑡−1, 𝐈𝐈𝑡𝑡, 𝐈𝐈𝑡𝑡+1} • Keyframes {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} – Visual Odometry (≈SfMLearner) • Photometric error + cycle consistency + depth smooth term – Keyframe selection • Triplet loss KFはどのように選ばれるのか？
- 29. 28 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset Model
- 30. 29 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {𝓘𝓘𝒔𝒔, 𝐈𝐈𝒑𝒑, 𝐈𝐈𝒏𝒏} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF ℐ𝑠𝑠 {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} 𝐈𝐈𝑡𝑡 Model KF pool 𝒫𝒫 𝐾𝐾 Dataset
- 31. 30 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, I𝑝𝑝, I𝑛𝑛} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} 𝐈𝐈𝑡𝑡 Model Loss Train
- 32. 31 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, I𝑝𝑝, I𝑛𝑛} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 {𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} 𝐈𝐈𝑡𝑡 Model Loss Train
- 33. 32 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(𝐈𝐈𝑝𝑝, 𝐈𝐈𝑡𝑡) > th: Insert tgt frame 𝐈𝐈𝒕𝒕 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 𝐈𝐈𝑝𝑝 𝐈𝐈𝑡𝑡 Model Score
- 34. 33 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(𝐈𝐈𝑝𝑝, 𝐈𝐈𝑡𝑡) > th: Insert tgt frame 𝐈𝐈𝒕𝒕 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 𝐈𝐈𝑝𝑝 𝐈𝐈𝑡𝑡 Model Score
- 35. 34 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(𝐈𝐈𝑝𝑝, 𝐈𝐈𝑡𝑡) > th: Insert tgt frame 𝐈𝐈𝒕𝒕 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 𝐈𝐈𝑝𝑝 𝐈𝐈𝑡𝑡 Model Score
- 36. 35 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(𝐈𝐈𝑝𝑝, 𝐈𝐈𝑡𝑡) > th: Insert tgt frame 𝐈𝐈𝒕𝒕 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset ℐ𝑠𝑠 𝐈𝐈𝑝𝑝 𝐈𝐈𝑡𝑡 Model Score
- 37. 36 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset 𝐈𝐈𝑛𝑛𝐈𝐈𝑝𝑝 Model ℐ𝑠𝑠 𝐈𝐈𝑡𝑡 Scores
- 38. 37 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, 𝐈𝐈𝑝𝑝, 𝐈𝐈𝑛𝑛} Train all the model if iteration > 200 & Similarity(I𝑝𝑝, I𝑡𝑡) > th: Insert tgt frame I𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset Model
- 39. 38 Proposed method • Training – Keyframe update & management Random KF initialization for epoch: for iteration: Choose training pair {ℐ𝑠𝑠, I𝑝𝑝, I𝑛𝑛} Train all the model if iteration > 200 & Similarity(𝐼𝐼𝑝𝑝, 𝐼𝐼𝑡𝑡) > th: Insert tgt frame 𝐼𝐼𝑡𝑡 as KF Merge KF KF pool 𝒫𝒫 𝐾𝐾 Dataset Model この操作を繰り返すことで KF poolの最適化を行う
- 40. 39 Experimental results • KITTI dataset – Monocular Depth Estimation KF selectionによって学習データを調整することで, 学習が安定し 推定精度も高くなる
- 41. 40 Experimental results • KITTI dataset – Monocular Depth Estimation KF selectionによって学習データを調整することで, 学習が安定し 推定精度も高くなる
- 42. 41 Experimental results • KITTI dataset – Absolute Trajectory Error (ATE) KF selectionがdata augmentationの効果を持ち, 結果としてカメラ 姿勢の推定精度が向上
- 43. 42 Experimental results • KITTI dataset – Average Rotation Errors とはいえカメラの回転の推定精度はORB-SLAM[Mur-Artal, TOR15]には 勝てていない状況
- 44. 43 Experimental results • KITTI dataset – Keyframe selection • カメラが並進する場所では, 均一になるように選択 • カメラが回転する場所では, 変化が激しいのでより刻んだ選択
- 45. 44 Experimental results • KITTI dataset – Ablation study Depth推定 カメラ軌跡推定
- 46. 45 Conclusion • SfMLearner with KF selection – VSLAMで最も重要なKF selectionを, SfMLearnerの枠組みに追加 – UnsupervisedでKF selectionを学習する手法を提案 – 従来手法よりも高精度な奥行き推定, カメラ姿勢推定を達成. • 感想 – 従来人手の緻密な設計が必要だったKF selectionを, unsupervisedに CNNで学習し実現した点が新しく非常に面白い – KF selectionだけでなく, Bundle Adjustment等の最適化要素も追加 できるとDeep SLAMの実現により近付きそう