Basics of Localization, Mapping and SLAM. Jari Saarinen Aalto University Department of Automation and systems Technology

Transcription

1 Basics of Localization, Mapping and SLAM Jari Saarinen Aalto University Department of Automation and systems Technology

2 Content Introduction to Problem (s) Localization A few basic equations Dead Reckoning Scan Matching

3 Introduction Knowing ones location is essential in most of the autonomous applications Enables closed-loop control from one location to another - Navigation The act of finding one's location against a map is known as localization Mapping is the act or process of making a map Simultaneous Localization and Mapping

4 Examples

5 What's involved?

6 Control Control

7 Motion Motion

8 State Motion Xt Motion model f(xt,ut) Xt+1

9 Observation

10 Representation Sensor model

11 Data Association?

12 Localization Localization is state estimation Given a map, find your location Some terminology Dead reckoning / Odometry = You estimate your own movement without reference Continous localization= You roughly know where you are in the map Global = you have no idea where you are in the map

13 Localization

14 Dead-Reckoning/Odometry Estimation of position without reference The position is integrated using motion model or measurements Encoders Gyros IMU...

15 Example Differential driven robot

16 Dead-reckoning

17 In more general form

18 Dead-Reckoning/Odometry

19 Noise/Errors

20 Evolution of noise No heading error With heading error

21 Covariance

22 Using Perception

23 2D ranging Sensor measures the distance to some target in some direction Most popular, Ultrasound and Laser range finders Laser is defacto nowadays

24 Laser Measurement

25 Measurement in world frame

26 Measurement Noise 0 zexp zmax

27 Exercise

28 Methods for scan (and map) matching

29 Scan Matching

30 Scan matching Assumptions: Measurements are taken from same environment, and are close to each other

31 Scan matching approaches Search in feature space Search in pose space Look for corresponding features and form transformation accordingly Find a pose that provides the best correlation Translation invariant transformations

32 Using odometry as initial guess

33 Iterated Closest Point (ICP) Assume that the closest point corresponds to correct one Compute transformation that minimizes the sum of squared distances (Closed form) Transform and repeat

34

35 Correlation in pose space 1. pick a pose from the search space; 2. transform the current scan accordingly; 3. calculate the score using a correlation function. Different correlation functions Binary - if the resulting cell occupation is the same in both increase correlation Distance to closest

36 2D correlation

37 Searchers Even space Coarse-to-fine / Iterative Branch-and-bound

38 Histogram Assumption angle histogram is the same in both scans, it is only sifted by the amount of rotation

39 Some reading material Sensors for Mobile Robots: Theory and Application H.R Everett Where am I?" Systems and Methods for Mobile Robot Positioning (J. Borenstein, H. R. Everett, and L. Feng) Probabilistic Robotics - Sebastian Thrun, Wolfram Burgard and Dieter Fox Mobile Robot Localisation and Mapping in Extensive Outdoor Environments Tim Bailey A Sensor-Based Personal Navigation System and its Application for Incorporating Humans into a Human-Robot Team - Jari Saarinen Algorithms for Mobile Robot Localization and Mapping,Incorporating Detailed Noise Modeling and Multi-Scale Feature Extraction Samuel T. Pfister

40 Outline for next week Maps Probabilistic localization Global localization algorithm SLAM basics Use cases?