```行動機器人的定位及SLAM導論
Source
Authors
Speaker
Date
： Simultaneous Localization and Mapping tutorial , Probabilistic Robotics , etc.
： HUGH DURRANT-WHYTE 、TIM BAILEY , MIT.Press , etc.
：余俊瑩
：洪國寶 老師
：100.03.21
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OUTLINE
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包含機器人的位置及方向
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Localization is the most fundamental problem to
providing a mobile robot with autonomous capabilities.
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1.使用單一感測器是不足的，必須整合多種感測器的資訊.
2.GPS的使用是局限的，以地圖為基礎技術(Map-based)是必須.
3.使用單一時間點的觀測是不足的，循序的估測(Sequential)是必須.
4.為了處理真實環境中種種不確定因素，使用機率型(Probabilistic)

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OUTLINE
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Local Localization or Position Tracking:機器人的初始狀態

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Global Localization:假設機器人所處的環境是已知的，然

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Kidnapped Robot Problem:考慮機器人狀態隨時是未知的，
A mobile robot must recover from localization failure.
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OUTLINE
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OUTLINE
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Motion Model
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1.里程計(Odometer)

2.GPS
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Measurement Model
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1.數位相機(Camera) :bear-only
2.聲納感測器(Sonar)
3.雷射測距儀(LRF)
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Map loop-closure: A robot returns to a previously mapped
region after a long excursion.
Loop detection and Global Tuning
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Scene Understanding and Localization
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-透過Sensors進行環境感知，藉由機器人接收sequential外部資訊使用
Probabilistic達到同步自行定位及環境地圖建置.
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SLAM seems like a chicken and egg problem — but we can
make progress if we assume the robot is the only thing that
moves
SLAM(Simultaneous Localization And Mapping)
SLAM also called concurrent mapping and
localisation(CML)
Main assumption: the world is static
EKF-SLAM (EKF filter)or Fast SLAM(Particle filter) 18

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In SLAM, both the trajectory of the platform and the
location of all landmarks are estimated online
At a time instant k , the following quantities are defined:
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The following a control Uk and observation Zk , is computed using Bayes
theorem.
This computation requires that a state transition model and an observation
model are defined describing the effect of the control input and observation
respectively.
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when the vehicle location and landmark locations are known
The observations are conditionally independent given the map and the current
vehicle state.
The motion model for the vehicle can be described in terms of a probability
distribution on state transitions in the form
The state transition is assumed to be a Markov process in which the next state
Xk depends only on the immediately preceding state Xk-1 and the applied
control Uk and is independent of both the observations and the map.
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recursive (sequential) prediction (time-update) correction (measurement-update)
Motion model
observation model
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This assumes that the location of the vehicle Xk is known (or at least
deterministic) at all times, subject to knowledge of initial location.
A map m is then constructed by fusing observations from different locations.
This assumes that the landmark locations are known with certainty, and the
objective is to compute an estimate of vehicle location with respect to these
landmarks.
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OUTLINE
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論文主要架構:
Camera
Camera
calibration
calibration
Feature
Feature
match??
match??
1.Apperant-based
2.Upward-looking camera
3.Infrared LEDs
4.LRF
5.Kinect

Motion model’s contorl

Odometer 或者控制樂高的伺服馬達

SLAM
SLAM
Path
Path
planning
planning
1.Shortest path
2.A*
3.Fuccy
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Q&A
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OUTLINE
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