SLAM文獻閱讀（不定期更新）
SLAM文獻閱讀
- [1] Comparison of Optimization Techniques for 3D Graph-based SLAM
- [2] AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation
- [3] A SLAM-based Approach for Underwater Mapping using AUVs with Poor Inertial Information
[1] Comparison of Optimization Techniques for 3D Graph-based SLAM
綜述性論文，針對Graph SLAM的總體框架、前端、後端分別進行描述。
- 前端分類為map to map (構建submap), image to map（通過點特徵確定相機相對於map的位姿）, image to image (相機影象匹配，simplest)；
- 後端中主要使用了三種工具：G2O（General (Hyper) Graph Optimization）、GTSAM（Georgia Tech Smoothing and Mapping）和HOG-Man（Hierarchical Optimization on Manifolds）
G2O: It is a C++ framework for performing the optimization of nonlinear least squares problems that can be embedded as a graph or in an hyper-graph. (工程常用)
GTSAM: It is a C++ library based on factor graphs. A factor graph consists of factors connected to variables. The factors represent probabilistic information on the unknown random variables in the estimation problem. （程式碼具有優秀的程式設計規範，適合學習， iSAM 求解器）
HOG-Man: It applies Gauss Newton with sparse Cholesky factorization that considers a manifold representation of the state space to better deal with the camera rotations. （劃分submap，多層圖結構）
[2] AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation
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sensors的論文感覺都挺長啊，這篇論文裡綜述部分整理的很有條理，表格如下
[23]. He, B.; Liang, Y.; Feng, X.; Nian, R.; Yan, T.H.; Li, M.H.; Zhang, S.J. AUV SLAM and experiments using a mechanical scanning forward-looking sonar. Sensors 2012, 12, 9386–9410.
[24]. Burguera, A.; Gonzalez, Y.; Oliver, G. Underwater SLAM with robocentric trajectory using a mechanically scanned imaging sonar. In Proceedings of the 2011 IEEE International Conference on Intelligent Robotics and Systems, San Francisco, CA, USA, 25–30 September 2011; pp. 3577–3582.
[25]. Aulinas, J.; Carreras, M.; Llado, X.; Salvi, J.; Garcia, R.; Petillot, Y.R. Feature extraction for underwater visual SLAM. In Proceedings of the 2011 IEEE Oceans, Santander, Spain, 6–9 June 2011; pp. 1–7.
[26]. Mallios, A.; Ridao, P.; Hernàndez, E.; Ribas, D.; Maurelli, F.; Petillot, Y.R. Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar. In Proceedings of the 2009 Europe Oceans, Bremen, Germany, 11–14 May 2009; pp. 1–6.
[27]. Barkby, S.; Williams, S.B.; Pizarro, O.; Jakuba, M. Incorporating prior bathymetric maps with distributed particle bathymetric SLAM for improved AUV navigation and mapping. In Proceedings of the MTS/IEEE Oceans Conference and Exhibition, Biloxi, MS, USA, 26–29 October 2009; Volume 2, pp. 1–7.
[28]. Ribas, D.; Ridao, P.; Tardós, J.D. Underwater SLAM in man made structured environments. J. Field Robot. 2008, 25, 898–921. [CrossRef]
[29]. Fairfield, N.; Wettergreen, D. Active localization on the ocean floor with multibeam sonar. In Proceedings of the MTS/IEEE OCEANS, Quebec City, Canada, 15–18 September 2008; pp. 1–10.
[30]. Roman, C.N.; Singh, H. A self-consistent bathymetric mapping algorithm. J. Field Robot. 2007, 24, 23–50.
[31]. Fairfield, N.; Kantor, G.; Wettergreen, D. Real-time slam with octree evidence grids for exploration in underwater tunnels. J. Field Robot. 2007, 24, 3–21.
[32]. Williams, S.; Mahon, I. Simultaneous localisation and mapping on the Great Barrier Reef. In Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, 26 April–1 May 2004; Volume 2, pp. 1771–1776.
[33]. Tena-Ruiz, I.; Raucourt, S.; Petillot, Y.; Lane, D.M. Concurrent mapping and localization using side-scan sonar. IEEE J. Ocean. Eng. 2004, 29, 442–456.
[34]. Williams, S.B. Efficient Solutions to Autonomous Mapping and Navigation Problems. Ph.D thesis, Australian Center for Field Robotics, University of Sydney, Sydney, Australia, 2001. -
Hector SLAM and GMapping :常用的兩種laser SLAM方法，有ROS工具包
Hector SLAM：基於Gauss-Newton法，單獨使用scan matching估計位置
http://wiki.ros.org/hector_slam （ROS package）
Gmapping：Rao-Blackwellized particle filter，使用里程計估計位置
http://wiki.ros.org/gmapping/ （ROS package） -
Augmented Extended Kalman Filter （AEKF）
相較於EKF，其改進在於認為系統噪聲均值不為0（仍滿足高斯分佈），在每一步對過程噪聲和測量噪聲的均值和方差進行修正（在update過程中）。 -
本文以考慮機器人x，y和首向角三自由度運動距離，測量資訊為測距，公式說明極其細緻，適合EKF SLAM的入門學習。
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European Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project：該專案中有水下機器人SLAM的部分，網址為
http://www.swarms.eu/index.html
[3] A SLAM-based Approach for Underwater Mapping using AUVs with Poor Inertial Information
- 這篇論文裡很有趣的部分在於將bathymetric SLAM 認為是一種feature SLAM方法，通過一些方法在點雲資料中提取特徵，並且匹配演算法還對錯誤的匹配據有魯棒性。
“In the past few years there has been great interest and progress made in the development of 3D point cloud feature extraction among the terrestrial robotics community. Many of these algorithms are closely related to 2D image features. This is not surprising, as many of the image feature algorithms treat images as if they were terrain maps, where the image intensity of a given pixel can be thought of as terrain height. Some well-known algorithms that use this idea are Harris Corners [16], SIFT [17], and SURF [18]. The general approach with all of these algorithms is that by considering not only a single point or pixel, but a small neighborhood of points and pixels, stable features can be identified and statistics extracted such that the feature can be identified and matched by later observations. Methods vary in their computational complexity, robustness to noise, invariance under various combinations of transformation, rotation, lighting, etc.” - 本文主要是在點雲中提取具有較高曲率的點作為特徵，使用面地形中的點雲進行特徵提取，理論依據主要是“ over short time scales, the effects of this warping are small, and can be ignored. Using these submaps built from data collected over short intervals, features can be extracted.”