行動機器人的定位及 SLAM 導論

Source ： Simultaneous Localization and Mapping tutorial , Probabilistic Robotics , etc.Authors ： HUGH DURRANT-WHYTE 、 TIM BAILEY , MIT.Press , etc.Speaker ：余俊瑩Advisor ：洪國寶 老師Date ： 100.03.21

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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一、 INTRODUCTION 行動機器人定位的問題是在於已知的環境地圖中估測機

器人的姿態 (Robot’s state) 包含機器人的位置及方向

Localization is the most fundamental problem to providing a mobile robot with autonomous capabilities.

機器人導航 (Path planning) 是使機器人完成自主任務的必要條件 .

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一、 INTRODUCTION 行動機器人定位是困難的，主要原因 :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)

演算法是必須 .

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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二、 PROBLEM OF LOCALIZATION Local Localization or Position Tracking: 機器人的初始狀態

是已知的，估測是有界限的 (Bounded).

Global Localization: 假設機器人所處的環境是已知的，然而缺乏機器人初始狀態，估測是無界限的 (Unbounded).

Kidnapped Robot Problem: 考慮機器人狀態隨時是未知的， A mobile robot must recover from localization failure.

靜態環境與動態環境 被動定位與主動定位 單一機器人定位與多機器人定位

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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三、 CHALLENGE OF LOCALIZATION 解決觀測與地圖的不一致性 (Inconsistence)

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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四、 SCENE UNDERSTANDING 描述真實世界的『不確定性 (Uncertainty) 』如控制器的

誤差、感測器的誤差及環境的變異性…等 .

環境感知的能力是行動型機器人完成自主任務的重要根基

使用 Motion Model and Observation Model 的機率方式，描述機器人運動與環境感測器的不確定性，進而保留其他可能性的彈性 .

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四、 SCENE UNDERSTANDING Motion Model 利用機率方式描述機器人行動的不正確性 藉由機器人的運動，預測 (Prediction) 其狀態

1. 里程計 (Odometer)

利用車輪轉動量以計算機器人的位移量

2.GPS

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四、 SCENE UNDERSTANDING Measurement Model 利用機率方式描述環境感測器的資料不正確性 藉由感測器量測之環境資訊，修正 (Correction) 其預測之狀態

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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四、 SCENE UNDERSTANDING Map loop-closure: A robot returns to a previously mapped

region after a long excursion. Loop detection and Global Tuning

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小結 機器人自主移動研發主要核心技術包含兩大層面 : Scene Understanding and Localization

當環境資訊是未知的或環境中的參考點不可用時，最常使用 SLAM(Simultaneous Localization And Mapping)

- 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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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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五、 SLAM 機器人定位與建地圖是一體兩面的問題 : 如果機器人沒

有事先獲的環境資訊的地圖，在未知環境中建地圖要仰賴可靠機器人的位置估測；然而欲得到機器人在環境中的位置又必須要有正確的環境地圖

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)

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五、 SLAM-PRELIMINARIES 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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五、 SLAM- PROBABILISTIC SLAM

The following a control Uk and observation Zk , is computed using Bayes theorem. This computation requires that a state transition model and an observationmodel are defined describing the effect of the control input and observation respectively.

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五、 SLAM- PROBABILISTIC SLAMThe observation model describes the probability of making an observation zk 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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五、 SLAM- PROBABILISTIC SLAMThe SLAM algorithm is now implemented in a standard two-step recursive (sequential) prediction (time-update) correction (measurement-update)

observation model

Motion model

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五、 SLAM- PROBABILISTIC SLAM

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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五、 SLAM- SOLUTIONS TO THE SLAM

利 用 MonoSLAM 並 結 合 EKF(Enhance Kalman Filter) 或 PF(Particle Filter) ，整合 sensors 進行機器人移動的預測及修正程序 .

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions

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六、 CONCLUSION論文主要架構 :

Camera calibration

Feature match??

SLAM

Path planning

Camera calibration

內在參數及外在參數求得 :

內在參數 : 描述攝影機座標與影像座標的轉換

外在參數 : 描述世界座標與攝影機座標的轉換

Feature match??

1.Apperant-based2.Upward-looking camera3.Infrared LEDs4.LRF5.Kinect

SLAM

Motion model’s contorl

Odometer 或者控制樂高的伺服馬達

Path planning

1.Shortest path2.A*3.Fuccy

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Q&A

Thanks for your attention

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OUTLINE一、 Introduction

二、 Problem of Localization

三、 Challenge of Localization

四、 Scene Understanding 1.Motion model(Prediction Model) 2.Observation Model (Sensor model)

五、 SLAM 1.Preliminary 2.Probabilistic SLAM 3.Solutions to the SLAM

六、 Conclusions