mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 19:48:02 -05:00
19 lines
841 B
ReStructuredText
19 lines
841 B
ReStructuredText
.. _`SLAM`:
|
||
|
||
SLAM
|
||
====
|
||
|
||
Simultaneous Localization and Mapping(SLAM) examples
|
||
Simultaneous Localization and Mapping (SLAM) is an ability to estimate the pose of a robot and the map of the environment at the same time. The SLAM problem is hard to
|
||
solve, because a map is needed for localization and localization is needed for mapping. In this way, SLAM is often said to be similar to a ‘chicken-and-egg’ problem. Popular SLAM solution methods include the extended Kalman filter, particle filter, and Fast SLAM algorithm[31]. Fig.4 shows SLAM simulation results using extended Kalman filter and results using FastSLAM2.0[31].
|
||
|
||
.. toctree::
|
||
:maxdepth: 2
|
||
:caption: Contents
|
||
|
||
iterative_closest_point_matching/iterative_closest_point_matching
|
||
ekf_slam/ekf_slam
|
||
FastSLAM1/FastSLAM1
|
||
FastSLAM2/FastSLAM2
|
||
graph_slam/graph_slam
|