Robust Vehicle Localization in Urban Environments Using Probabilistic MapsKitsukawa Yuki
研究室ゼミでの論文紹介資料です。
Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
Jesse Levinson, Sebastian Thrun
International Conference on Robotics and Automation (ICRA), 2010
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
出典:Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
Facebook AI
公開URL : https://arxiv.org/abs/2005.12872
概要:Detection Transformer(DETRという)という新しいフレームワークによって,non-maximum-supressionやアンカー生成のような人手で設計する必要なく、End-to-Endで画像からぶった検出を行う手法を提案しています。物体検出を直接集合予測問題として解くためのtransformerアーキテクチャとハンガリアン法を用いて二部マッチングを行い正解と予測の組み合わせを探索しています。Attentionを物体検出に応用しただけでなく、競合手法であるFaster R-CNNと同等の精度を達成しています。
Robust Vehicle Localization in Urban Environments Using Probabilistic MapsKitsukawa Yuki
研究室ゼミでの論文紹介資料です。
Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
Jesse Levinson, Sebastian Thrun
International Conference on Robotics and Automation (ICRA), 2010
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
出典:Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
Facebook AI
公開URL : https://arxiv.org/abs/2005.12872
概要:Detection Transformer(DETRという)という新しいフレームワークによって,non-maximum-supressionやアンカー生成のような人手で設計する必要なく、End-to-Endで画像からぶった検出を行う手法を提案しています。物体検出を直接集合予測問題として解くためのtransformerアーキテクチャとハンガリアン法を用いて二部マッチングを行い正解と予測の組み合わせを探索しています。Attentionを物体検出に応用しただけでなく、競合手法であるFaster R-CNNと同等の精度を達成しています。
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Studies have shown that meditating for just 10-20 minutes per day can have significant positive impacts on both mental and physical health over time.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses techniques for interpreting and explaining errors in deep neural network object detection models. It describes recent work on feature attribution methods like TCAV that allow quantitative evaluation of what concepts a model has learned. The document also discusses new research on using these interpretation methods to analyze false positives made by object detectors and generating concept-based explanations of errors. Future work directions include developing automated techniques for explanation and reducing false positives.
This document presents a method for downsampling point cloud data to enable real-time scan matching for autonomous vehicles. It introduces two new downsampling algorithms: Ring Random Filter and Distance Voxel Grid Filter. It evaluates the algorithms based on execution time of scan matching, downsampling time, and relative error compared to raw point cloud data from tests in suburban and city environments. The results show the downsampling enables real-time scan matching with relative errors generally less than 10 cm.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
輪講発表資料です。
The Multikernel: A new OS architecture for scalable multicore systems
Andrew Baumann, Paul Barham, Pierre-Evariste Dagand, Tim Harris, Rebecca Isaacs, Simon Peter, Timothy Roscoe, Adrian Schüpbach, and Akhilesh Singhania
SoSP09
An Analysis of Linux Scalability to Many Cores
Silas Boyd-Wickizer, Austin T. Clements, Yandong Mao, Aleksey Pesterev, M. Frans Kaashoek, Robert Morris, and Nickolai Zeldovich
MIT CSAIL
OSDI10
輪講発表資料です。
S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters
Ammar Ahmad Awan, Khaled Hamidouche, Jahanzeb Maqbool Hashmi, Dhabaleswar K. Panda
PPoPP ‘17 Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming Pages 193-205
5. 先行研究
[1] Yan Yan, Yuxing Mao, and Bo Li. SECOND: Sparsely EmbeddedCconvolutional Detection. Sensors, 18(10):3337, 2018
[2] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4490–4499, 2018. 5
SECOND[1]
● VoxelNet[2]の精度を落とさず4.6倍高速化
● 他の既存手法と比較し精度は劣るが実行時間が短い
● 車・自転車・歩行者のマルチクラス検出に非対応
8. Voxel Encoder Network(VEN)の改良
クラス検出
位置推定
方向推定
8Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4490–4499, 2018.
● 転移畳込みによる計算の効率化
9. Sparse Middle Network(SMN)の改良
● フィルタリングで起こる特徴量減少の抑制
9
クラス検出
位置推定
方向推定
[2] Benjamin Graham and Laurens van der Maaten. Submanifold sparse convolutional networks. arXiv preprint arXiv:1706.01307, 2017.
[3] Ben Graham. Sparse 3d convolutional neural networks. arXiv preprint arXiv:1505.02890, 2015.
10. Regison Proposal Network(RPN)の改良
10
クラス検出
位置推定
方向推定
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks.
In European conference on computer vision, pages 630–645. Springer, 2016.
● ショートカットコネクションにより正確な位置情報の伝播
11. 評価
● KITTI 3D Object Detection Dataset [4] の学習用データを
train/testに分割
● 評価指標: Average Precision
● 対象クラス: 車・自転車・歩行者
● レベル: Easy, Moderate, Hard
11
CPU Intel Xeon Gold 6126 2.60GHz
GPU NVIDIA Tesla V100 16GB
実行環境 VEN Voxel Encoder Network
SMN Sparse Middle Network
RPN Region Proposal Network
ネットワークの略称
[4] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite.
In Conference on Computer Vision and Pattern Recognition (CVPR), 2012