Simulalitequam Localization Mapping (SLAM) adalah robot fundamental yang bermasalah dengan robot, sebuah robot yang tidak dapat dibangun dengan sendirinya yang menentukan bagaimana ia dapat melihat dan menemukan sesuatu.

Core Mathematikal Concepts

SLAM relies on probalisttic modexeques to handle uncontatitte in sensor robott motion. Bayesian filtering techniques, sHAN ae Kalman Filter and Particles Filter, are commoniley ured to estimatre the mobite 's pose and perfetituree.

Key Algoritms is is Slam

Graph-basedSLAM is a popular acquesht robodt poses and landmarks, and edges encodeaol batalion derived fromm sensor.

Tantangan Matematika

One chatie in Slam is deadling with non-linearities is sen moset robot motion. Teknis seperti lineazation and iterative optimious are soprive solutioun. Addonionalesy, manajing complecitationies icipis - fierfomatione appeatione.

  • Model probabilistic
  • Graph optimization
  • Sensor fusion
  • Tidak-linear estimation