RDL VL 06 Localization

Localization
What is Localization?

Given: Map (model) of the environment.
Task: Estimate the robot pose relative to the given map.
Robot pose:

position (xR, yR)

orientation θ.

Typs of Localization

with mapping ⇒ SLAM

Sensors for Localization

Proprioceptive Sensors

Wheel Encoder → Wheel odometry

Inertial Measurement Unit (IMU) → Inertial odometry
Exteroceptive Sensors

Camera → Visual odometry

Laser scanner, sonar → Scan matching (SLAM)

particle filter
Overview

Based on Bayesian statistics.
Methods: Kalman filter, particle filter (Monte Carlo Localization).
Sensors measurements: wheel encoder, IMU, GPS, laser scanner, camera, etc.

Pros:

Model sensor noise.

Fuse multimodal sensor data.

Cons:

Complex algorithms and models.

Computationally expensive.

Function Approximation

Probabilistic method used for non-parametric function approximation.
An arbitrary function can be described by a set of $M$ particles at time $t$ :

$X_{t} := {x_{t}^{(1)}, x_{t}^{(2)}, ..., x_{t}^{(M)}}$

Pros

Estimates any posterior distribution (i.e. not limited to Gaussian distribution).

Able to cope with noisy sensor data and inaccurate odometry.

Easy to implement.

Cons:

Large number of particles slows down localization.

Requires large storage space.

High computational resources.

Further Problems

Adaptive Monte Carlo Localization

AMCL ROS Package

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