Durrant-Whyte & Bailey "Simultaneous localization and mapping"

Durrant-Whyte, H. &  Bailey, T.
(Australian Centre for Field Robotics, Sydney Univ., NSW)
Simultaneous localization and mapping
Robotics & Automation Magazine, 2006

Simultaneous localization and mapping: part I

SLAMpart1.pdf

Robotics & Automation Magazine, IEEE, Volume: 13,  Issue: 2 (June 2006)

Simultaneous localization and mapping (SLAM): part II

SLAMpart2.pdf

Robotics & Automation Magazine, IEEE, Volume: 13,  Issue: 3 (Sept. 2006)


Table 1. Open-source SLAM software.

Kai Arras
The CAS Robot Navigation Toolbox, a MATLAB simulation toolbox for robot localization and mapping
http://www.cas.kth.se/toolbox/index.html

Tim Bailey
MATLAB simulators for EKF-SLAM, UKF-SLAM, and FastSLAM 1.0 and 2.0. http://www.acfr.usyd.edu.au/homepages/academic/tbailey/software/index.html

Mark Paskin
Java library with several SLAM variants, including Kalman filter, information filter, and thin junction tree forms. Includes a MATLAB interface.
http://www.stanford.edu/~paskin/slam/

Andrew Davison
Scene, a C++ library for map-building and localization. Facilitates real-time single camera SLAM.
http://www.doc.ic.ac.uk/~ajd/Scene/ index.html

José Neira
MATLAB EKF-SLAM simulator that demonstrates joint compatibility branch-and-bound data association.
http://webdiis.unizar.es/~neira/software/slam/slamsim.htm

Dirk Hähnel
C language grid-based version of FastSLAM.
http://www.informatik.uni-freiburg.de/~haehnel/old/download.html

Various
MATLAB code from the 2002 SLAM summer school.
http://www.cas.kth.se/slam/toc.html

Table 2. Online datasets.

Eduardo Nebot
Numerous large-scale outdoor datasets, notably the popular Victoria Park data.
http://www.acfr.usyd.edu.au/homepages/academic/enebot/dataset.htm

Chieh-Chih Wang
Three large-scale outdoor datasets collected by the Navlab11 testbed.
http://www.cs.cmu.edu/~bobwang/datasets.html

Radish (The Robotics Many and varied indoor datasets, including large-area Data Set Repository) data from the CSU Stanislaus Library, the Intel Research Lab in Seattle, the Edmonton Convention Centre, and more.
http://radish.sourceforge.net/

IJRR (The International Journal of Robotics Research)
IJRR maintains a Web page for each article, often containing data and video of results. A good paper example is by Bosse et al. [3], which has data from Killian Court at MIT.
http://www.ijrr.org/contents/23\_12/abstract/1113.html

IEEE Robotics and Automation Society http://www.ieee-ras.org/
IEEE ICRA (International Conference on Robotics and Automation) http://www.icra2009.org/
http://icra2010.grasp.upenn.edu/

International Foundation of Robotics Research http://www.ifrr.org/
ISRR 2009 - 14th International Symposium on Robotics Research http://www.isrr2009.ethz.ch/

IROS 2009: The 2009 IEEE/RSJ International Conference on Intelligent RObots and Systems www.iros09.mtu.edu/
http://www.iros2010.org.tw/

ICARCV 2010 - The 11th International Conference on Control, Automation, Robotics and Vision
http://www.icarcv.org/2010/

History

- 1986, probabilistic SLAM problem (IEEE Robotics and Automation Conference)
Peter Cheeseman, Jim Crowley, and Hugh Durrant-Whyte, Raja Chatila, Oliver Faugeras, Randal Smith
> estimation-theoretic methods, consistent mapping

- consistent probabilistic mapping
Smith and Cheesman [39] and Durrant-Whyte [17]
> statistical basis
"There must be a high degree of correlation between estimates of the location of different landmarks in a map"

- visual navigation & sonar-based navigation
Ayache and Faugeras [1],  Crowley [9] and Chatila and Laumond [6]
> Kalman filter-type algorithms

Smith et al. [40]

smith90stochastic.pdf

"The estimations of the landmarks are all necessarily correlated with each other because of the common error in estimated vehicle location."

> joint state (of the vehicle pose and every landmark position) to be updated following each landmark observation & estimator (state vector)

- random-walk behavior with unbounded error growth (without knowledge of the convergence behavior of the map)

> single estimation problem: "The combined mapping and localization problem is convergent."
"The more the correlations between landmarks grew, the better the solution."

- 1995, coining of SLAM (a paper at the International Symposium on Robotics Research) or called CLM (concurrent mapping and localization)
Csorba [10], [11]. the Massachusetts Institute of Technology [29], Zaragoza [4], [5], the ACFR at Sydney [20], [45], and others [7], [13]
> computational efficiency, addressing in data association, loop closure

- 1999 ISRR, convergence between the Kalman-filter-based SLAM methods and the probabilistic localisation and mapping methods introduced by Thrun

- 2000 IEEE ICRA
> algorithmic complexity, data association, implementation

Formulation

SLAM = process by which a mobile robot can build a map of an environment and at the same time use this map to deduce its location
(In SLAM, both the trajectory of the platform and the location of all landmarks are estimated online without the need for any a priori knowledge of location.)

Probabilistic SLAM
The SLAM probability distribution = the joint posterior density of the landmark locations and vehicle state (at time k) given the recorded observations and control inputs up to and including time k together with the initial state of the vehicle

recursive solution
: observation model + motion (state transition) model == Bayes theorem ==> estimate of SLAM distribution

   observation model -> prediction (; measurement update)
+ motion model -> correction (; time update)
+ Markov process
=> map building problem + localization problem
: joint posterior density of the landmark locations and vehicle state

As the map is built, the location accuracy of the robot measured relative to the map is bounded only by the quality of the map and relative measurement sensor.
: Robot relative location accuracy becomes equal to the localization accuracy achievable with a given map.

Solutions

1)
state-space model + additive Gaussian noise
EKF = extended Kalman filter

2)
a set of samples of a more general non-Gaussian probability distribution to describe vehicle motion
Rao-Blackwellized particle filter or FastSLAM algorithm

3)
information-state form

ref. Sebastian Thrun, Yufeng Liu, Daphne Koller, Andrew Y. Ng, Zoubin Ghahramani, Hugh Durrant-Whyte
Simultaneous Localization and Mapping With Sparse Extended Information Filters

1) EKF-SLAM



2) Rao-Blackwellized filter