collaborative visual slam framework for a multi-robot system€¦ · sensor: ueye monocular camera...

Collaborative Visual SLAMFramework for a Multi-Robot

System

Nived Chebrolu, David Marquez-Gamez andPhilippe Martinet

7th Workshop on Planning, Perception and Navigation for Intelligent VehiclesHamburg, Germany

28th September, 2015

1 / 27

Motivation for a collaborative system

Multi-robot system for disaster relief operations1

1Picture taken from project SENEKA, Fraunhofer IOSB2 / 27

Contribution of this paper

System For Collaborative Visual SLAM

3 / 27

Perception sensor

Monocular Camera

4 / 27

Main components of the system

Monocular Visual SLAM

Place Recognition System

Merging Maps

Collaborative SLAM Framework

5 / 27

Monocular visual SLAM

GoalGiven a sequence of images, obtain the trajectory ofthe camera and the structure/model of theenvironment.

Mono SLAM PTAM DTAM

6 / 27

Large-Scale Direct Visual SLAM

LSD-SLAM Output

7 / 27

Monocular SLAM: System Overview

Tracking Depth Estimation Map Optimization

8 / 27

Monocular Visual SLAM

Place Recognition System

Merging Maps

Collaborative SLAM Framework

9 / 27

Place Recognition System: Context

Where?Where?

Is the place already visited?

10 / 27

FAB-MAP Approach

Overlap Detection Scheme

11 / 27

Experimental Results - A Simple Scenario

Image Num. P(Seen) P(New)1 0.991 0.0012 0.085 0.9103 0.922 0.0024 0.991 0.0015 0.911 0.131

12 / 27

Monocular Visual SLAM

Place Recognition System

Merging Maps

Collaborative SLAM Framework

13 / 27

Merging Maps: Context

What is the transformation between two views?

14 / 27

Procedure for Merging Maps

Initial EstimateUsing

Horn's Method

Refine EstimateUsing Direct Image Alignment

Refine Final EstimateUsing ICP

15 / 27

Experimental Results: Input

(a) First Image (b) Second Image

(c) Depth map forfirst image

(d) Depth map forsecond image

16 / 27

Experimental Results: Output

(e) Before ApplyingTransformation

(f) After ApplyingTransformation

17 / 27

Monocular Visual SLAM

Place Recognition System

Merging Maps

Collaborative SLAM Framework

18 / 27

Overall Scheme

Figure: Overall scheme of our collaborative SLAM system

19 / 27

Experimental Results

Case study: Experimental Settings

Robotic Platform: Two Turtlebots.

Sensor: uEye Monocular Camera with wide-eyelens.

Images: 640 × 480 pixels @ 30 Hz

Environment: Area: 20m × 20m, Indoor(Semi-Industrial)

Computation: Core 2 Duo Laptop

Software: ROS, OpenCV, g 2o library.

20 / 27

At Instance 1

(a) Robot R1

(b) Robot R2

21 / 27

At Instance 2

(c) Robot R1

(d) Robot R2

22 / 27

At Instance 3

(e) Robot R1

(f) Robot R2

23 / 27

Global Map

(g) Combined Trajectory (h) Combined Depth Map

Global map computed at the central server.

24 / 27

Summary

A collaborative visual SLAM framework with:1 Monocular SLAM process for each robot.2 Detection of scene overlap amongst several

robots3 Global map computation fusing measurements

from all robots.4 Feedback mechanism for global information to

be communicated back to each robot.

25 / 27

Scope For Future Work

1 Investigate advantage due to feedback in termsof localization accuracy and map quality.

2 Towards a Decentralized system: Direct robotto robot communication.

3 Adapting for a hybrid team of robots (ex.UAVs and ground robots).

26 / 27

Thank you

Thank you very much for your attention !!!

Q&A

27 / 27