Mapping & SLAM¶

Mapping¶

Computer vision and cartography

Goal for an autonomous robot is to be able to construct (and/or use) a map/floor plan and to localize itself and its recharging bases.

SLAM¶

Simultaneous Localization and Mapping

Method for autonomous vehicles that enables building a map and localization of vehicle in that map at the same time.

Allow vehicle to map out unknown environments

flowchart LR
sd[/Sensor<br/>Data/] --> fe --> be --> pose[Pose Graph <br/>& Map Information]

subgraph fe["Frontend<br/>(Sensor-Dependent)"]
    direction LR
    me["Motion<br/>Estimation"] 
    ole["Obstacle<br/>Location<br/>Estimation"]
end

subgraph be["Backend<br/>(Sensor-Independent)"]
    direction LR
    rpg["Register<br/>Pose Graphs"]
    go[Graph<br/>Optimization]
end

	Localization	Mapping	SLAM
given	Map	object’s trajectory (position at each time)
Use sensor data to	estimate current position of object	map	Build map and estimate trajectory

Need for Map¶

• Path planning
• Limiting error in state estimates, by providing opportunity to ‘reset’

2D Graph SLAM¶

Considering uncertainty in \(x\) and \(y\), gaussian functions are applied to maximize probability of product $$ \mu = \Omega^{-1} \epsilon $$ where

• \(\mu =\) locations of landmarks and robot positions
• \(\Omega=\) matrix of \(X\) and landmarks
• \(\epsilon=\) vector of constraints

Constraints¶

• Initial constraint
• Relative motion constraint
• Relative measurement constraint

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