This video shows the operation of the Atlas framework for real-time, large-scale mapping using the MIT Killian Court data set. Atlas employed graphs of coordinate frames. Each vertex in the graph represents a local coordinate frame, and each edge represents the transformation between adjacent local coordinate frames. In each local coordinate frame, extended Kalman filter SLAM (Chap. 46.3.1, Springer Handbook of Robotics, 2nd edn 2016) is performed to make a map of the local environment and to estimate the current robot pose, along with the uncertainties of each. Each map's uncertainties were modelled with respect to its own local frame. Probabilities of entities in relation to arbitrary map-frames were generated by following a path formed by the edges between adjacent map-frames, using Dijkstra's shortest path algorithm. Loop-closing was achieved via an efficient map matching algorithm. Reference: M. Bosse, P. M. Newman, J. Leonard, S. Teller: Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework, Int. J. Robot. Res. 23(12), 1113-1139 (2004).

This video provides an illustration of graph-based SLAM, described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016). Reference: U. Frese: Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping, Auton. Robot. 21(2), 103–122 (2006).

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), performed on the campus of the University of Freiburg, Germany.

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), and performed in a parking garage in Stanford, CA.

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the MIT Killian Court data set. Reference: E. Olson, J. Leonard, S. Teller: Fast iterative alignment of pose graphs with poor initial estimates, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Orlando (2006), pp. 2262 - 2269; doi: 10.1109/ROBOT.2006.1642040.

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the HOGMAN algorithm. Reference: G. Grisetti, R. Kuemmerle, C. Stachniss, U. Frese, C. Hertzberg: Hierarchical optimization on manifolds for online 2-D and 3-D mapping, IEEE Int. Conf. Robot. Autom. (ICRA), Anchorage (2010), pp. 273-278; doi: 10.1109/ROBOT.2010.5509407.

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the TORO algorithm. Reference: G. Grisetti, C. Stachniss, S. Grzonka, W. Burgard. A tree parameterization for efficiently computing maximum likelihood maps using gradient descent, Proc. Robot. Sci. Syst. (RSS), Atlanta (2007)

This video shows an illustration of pose-graph SLAM optimization, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using sparse pose adjustment. Reference: K. Konolige, G. Grisetti, R. Kümmerle, W. Burgard, B. Limketkai, R. Vincent: Sparse pose adjustment for 2-D mapping, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Taipei (2010).