Localization using uni-axial laser rangefinder and IMU for MAV

Localization using uni-axial laser rangefinder and IMU for MAV

Introduction

UAV and MAV ● In recent years, research on Unmanned Aerial vehicles (UAV) and Micro aerial vehicles (MAV) has become more popular. Introduction 3

Localization ①Scanning the environment ②Matching the point cloud data to the map ③Localization Point cloud data Scanning Matching map and point cloud sata Map Introduction 4

Laser scanner Hokuyo UTM-30LX 210g Velodyne HDL-64e 13kg Introduction SICK LMS210 4.5kg 5

Size and Weight 30cm 1.3kg Introduction 6

Laser scanner Hokuyo UTM-30LX 210g Velodyne HDL-64e 13kg Introduction SICK LMS210 4.5kg 7

Purpose UAV Localization using rotation UAV miniaturization and weight reduction Introduction

MAV System 9

Equipment MAV System 10

System Uniaxial LRF 12V Supply 22.2V Battery USB USB Wifi Navigation USB Auto pilot APM2.6 BeagleBoard-xM ESC ESC ESC ESC Motor モータ モータ モータ 5V Supply MAV System 11

Attitude and Vertical control ● PID Attitude Control system Pitch/Roll Varying left/right thrust causes angular velocity Yaw One pair CW One pair CCW Vertical Up Gravity Difference of thrust causes pitch/roll rate Difference of yaw moment causes rotation Move forward/backward Down Gravity Control 12

Localization 13

Monte Carlo Localization Step 1 Step 2 Likelihood update Initial distribution Compare robot's environment with particles' environment Particles Robot Step 3 Resampling and Position estimation Resample particles by their likelihood to estimate robot's position Step 4 Motion prediction All particle move according to a probabilistic motion model Return to Step 2 14

Monte Carlo Localization Localization 15

Quadrotor motion model 2 x k +1=0.5 a x Δ t +v x Δ t + x k k 2 y k +1 =0.5 a y Δ t +v y Δ t + y k v x =a x Δ t +v x v y =a y Δ t +v y ψk +1= p Δ t + ψk k k +1 k k k +1 k k Position, Velocity and Yaw angle x k , y k , v x ,v y , ψk Control a x , a y , r (Yaw rate) k k Localization 16

Measurement model (A) (B) (C) (D) Measurement model = A + B + C + D Localization 17

Scanning by Maneuver Set distance between each point ● Rotation at each point ● Fixed UAV altitude ● Rotation points Localization 18

Experiment 19

Experiment environment Created using UTM-30LX Experiments 20

Simulation result Initial Particle distribution Resampling after UAV rotation Success estimation Movement without rotation resulted in expansion of particle distribution 21

Simulation 22

Experimental Result 23

Conclusion and Future work ● ● ● ● We achieved successful localization using a uniaxial laser rangefinder by applying Monte Carlo Localization. In order to self-localize, the UAV has to rotate. We will next add equipment to mechanically obtain the UAV`s true position to acquire a more accurate comparison between the experimental and theoretical results. We will strive to achieve autonomous flight where the UAV will decide the best actions by itself. 24