# Related Works of SLAM

Key Words: SLAM (Simultaneous Localization and Mapping)

Taxonomy

• Methods:
• Kalman filter (high computational complexity. [AAAI-2002-FastSLAM]);
• Particle filter;
• Data Source:
• Mobile phone sensor (Wi-Fi, Mag., Acc., Gyro.)

• SLAM applications:
• Applications in all scenarios in which a prior map is not available and needs to be built [IEEE-2016-Tutorial].
• Basic assumptions in SLAM [IEEE-2001-TRA]:
• The robot equipped with a sensor capable of making measurements of the landmark’s locations relative to the robot.
• The vehicle starts at an unknown location with no knowledge of the location of landmarks in the environment.
• The kinematic model of the robot is known (direction/speed/distance).
• The absolute locations of the landmarks are not available.
• The landmarks are stationary.
• As the robot moves through the environment (in a stochastic manner) it makes relative observations of the landmarks’ location.
• Differences compared with couriers’ indoor localization.
• The kinematic model (direction/speed/distance) of the couriers are unknown. Dead reckoning is impossible duo to high-sampling rate unacceptable.

Papers

• A Solution to the Simultaneous Localization and Map Building (SLAM) Problem, [IEEE-2001-TRA], Highly-cited.
• Taxonomy: Kalman filter; mmWave Radar;
• Important conclusions:
• Shows that a converged solution of SLAM is possible.
• Given the exact location of any one landmark, the location of any other landmark in the map can also be determined with absolute certainty.
• FastSLAM, [AAAI-2002-FastSLAM], Highly-cited.
• Taxonomy: Particle filter;
• Acceleterate SLAM with a new algorithm.
• Simultaneous Localization and Mapping: Part I, [IEEE-2006-Tutorial-1], Highly-cited.
• Tutorial paper. Brief introduction of the SLAM history, formulation and solution.
• Simultaneous localization and Mapping (SLAM): Part II, [IEEE-2006-Tutorial-2], Highly-cited.
• Tutorial paper. Focus on computational complexity, data association, and environment representation.
• SemanticSLAM, [IEEE-2016-SemanticSLAM]
• Taxonomy: Particle filter, Mobile phone sensors.
• Leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors.
• Floorplan needed.
• Dead reckoning used to estimated user’s kinematic.
• Past, Present, and Future of SLAM, [IEEE-2016-Tutorial], Highly-cited.
• Very good tutorial/survey paper on the recent progress of SLAM.
• Explore the third age of SLAM– robust perception age – based on the summary of previous works.
• The impact of deep learning is also discussed in the paper.

## Ref.

[IEEE-2001-TRA] Dissanayake, MWM Gamini, Paul Newman, Steve Clark, Hugh F. Durrant-Whyte, and Michael Csorba. “A solution to the simultaneous localization and map building (SLAM) problem.” IEEE Transactions on robotics and automation 17, no. 3 (2001): 229-241.

[AAAI-2002-FastSLAM] Montemerlo, Michael, Sebastian Thrun, Daphne Koller, and Ben Wegbreit. “FastSLAM: A factored solution to the simultaneous localization and mapping problem.Aaai/iaai 593598 (2002).

[IEEE-2006-Tutorial-1] Durrant-Whyte, Hugh, and Tim Bailey. “Simultaneous localization and mapping: part I.IEEE robotics & automation magazine 13, no. 2 (2006): 99-110

[IEEE-2006-Tutorial-2] Bailey, Tim, and Hugh Durrant-Whyte. “Simultaneous localization and mapping (SLAM): Part II.IEEE robotics & automation magazine 13, no. 3 (2006): 108-117.

[IEEE-2016-SemanticSLAM] Abdelnasser, Heba, Reham Mohamed, Ahmed Elgohary, Moustafa Farid Alzantot, He Wang, Souvik Sen, Romit Roy Choudhury, and Moustafa Youssef. “SemanticSLAM: Using environment landmarks for unsupervised indoor localization.IEEE Transactions on Mobile Computing 15, no. 7 (2015): 1770-1782.

[IEEE-2016-Tutorial] Cadena, Cesar, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, José Neira, Ian Reid, and John J. Leonard. “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age.” IEEE Transactions on robotics 32, no. 6 (2016): 1309-1332.