pose graph 估计实验和机器学习应用场景

玩乐：

用pose graph 估计相机位姿的实验

目前开源的SLAM RGB-D相机方案主要有：
DTAM https://github.com/anuranbaka/OpenDTAM
DVO https://github.com/tum-vision/dvo_slam
RTAB-MAP https://github.com/introlab/rtabmap
RGBD-SLAM-V2 https://github.com/felixendres/rgbdslam_v2
Elastic Fusion https://github.com/mp3guy/ElasticFusion
由于年代都比较久远，下了一个RTAB-MAP的源码不会修改到当前版本，就去YouTube看了下视频，神秘代码如下：
https://www.youtube.com/watch?v=71eRxTc1DaU&feature=youtu.be
2017年用联想手机扫描产生三维重建的图形，虽然比较粗糙，但是对于手机来说已经足够轻量级

看完了十四讲，也拟定了方向，基于语义分割(机器学习)的室内场景重建

语义重建的一些实验结果，语义分割其实现在已经到了一个高峰期，强化学习等手段层出不穷
传统非机器学习做法是构建物品数据库，直接将观测数据与数据库的样本进行比较[1,2 ]
【1】R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. J. Kelly, and A. J. Davison,
“Slam++: Simultaneous localisation and mapping at the level of objects,” 2013 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1352–9, 2013.
【2】R. F. Salas-Moreno, B. Glocken, P. H. Kelly, and A. J. Davison, “Dense planar slam,” inMixed and Augmented Reality (ISMAR), 2014 IEEE International Symposium on, pp. 157–164, IEEE, 2014.
尝试去构建语义地图[3, 4, 5, 6]
【3】A. Anand, H. S. Koppula, T. Joachims, and A. Saxena, “Contextually guided semantic la-beling and search for three-dimensional point clouds,” The International Journal of Robotics Research, p. 0278364912461538, 2012.
【4】J. Stückler, N. Biresev, and S. Behnke, “Semantic mapping using object-class segmentation of rgb-d images,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3005–3010, IEEE, 2012.
【5】I. Kostavelis and A. Gasteratos, “Learning spatially semantic representations for cognitive robot navigation,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1460–1475, 2013.
【6】C. Couprie, C. Farabet, L. Najman, and Y. LeCun, “Indoor semantic segmentation using depth information,” arXiv preprint arXiv:1301.3572, 2013.
现代由于机器学习的发展，开始使用神经网络，深度学习强化学习的方法去对图像进行准确的识别，检测和分割[144, 145, 146, 147, 148, 149]
【7】 J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale
hierarchical image database,” in CVPR09, 2009.
【8】 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012.
【9】 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385, 2015.
【10】S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems, pp. 91–99, 2015.
【11】J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmen-
tation,” arXiv preprint arXiv:1411.4038, 2014.
甚至构建地图本身的位姿估计和回环检测[13, 14, 15]
【13】 K. Konda and R. Memisevic, “Learning visual odometry with a convolutional network,” in International Conference on Computer Vision Theory and Applications, 2015.
【14】 A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 2938–2946, 2015.
【15】 Y. Hou, H. Zhang, and S. Zhou, “Convolutional neural network-based image representation for visual loop closure detection,” arXiv preprint arXiv:1504.05241, 2015.