Matlab implementation of adaptive monte carlo localization (AMCL)

Figure 1 Robot estimated position and particle position

Figure 2 Robot true and estimated trajectory (right), Number of particles and visible landmarks (left)

System model:

System kinematics:

Dataset:

• dataset/dataset2.mat: robot odometry and lidar measurements.
• dataset/AMCL.mat: AMCL particle filter result.

File structure:

• mainAMCL.m: estimation using AMCL method
• mainPF.m: estimation using particle filter method
• mainEKF.m: estimation using EKF method
• GifGeneration.m: gif generation

Reference:

• [1] M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," in IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, Feb. 2002, doi: 10.1109/78.978374.
• [2] Lang, H., Li, T., Villarrubia, G., Sun, S., Bajo, J. (2015). An Adaptive Particle Filter for Indoor Robot Localization. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_5

Cite this repository:

• text citation:

GitHub. 2022. GitHub - LonghaoQian/ParticleFilter: Particle filter for a robot in landmarks. [online] Available at: <https://github.com/LonghaoQian/ParticleFilter> [Accessed 2 May 2022].

• bibliography:

@misc{qian_2022_matlab,
  author = {Qian, Dr Longhao},
  month = {05},
  title = {Matlab Implementation of AMCL},
  url = {https://github.com/LonghaoQian/ParticleFilter},
  year = {2022},
  organization = {GitHub}
 }