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Xiaorui Zhu (presenter), Chunxin Qiu, Yulong Tao, Qi Jin Harbin Institute of Technology Shenzhen Graduate School, China

Cloud Based Localization for Mobile Robot in Outdoors

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Outline Background 1

Cloud-based Localization 2

Conclusion 4

Experiments 3

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Localization is an important problem for outdoor mobile robot

1.Traditional localization techniques: GPS  Advantage: no accumulative error  Disadvantage: signals unavailable in some cases

Laser scanner  Advantage: high precision  Disadvantage: time-consuming

Vision system  Advantage: ample information  Disadvantage: dependent on the illumination ; time-consuming

Background

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2. Long-term autonomous: Challenges: • Adapt to the dynamic scenarios

•  Detect and track static or moving objects (Zhao et al., 2008 ) •  Learn more knowledge to predict the environmental changes

(Sunderhauf et al., 2012) • Run a long time Require a large storage space Increase computational payloads

3. Cloud robotics: • Offload computation: SLAM: feature extraction and filter for the state estimation (Arumugam et al., 2010) • Access to large databases: Recognize and grasp objects: preprocess the object and access the large databases (Kehoe et al., 2013) • Share the knowledge : Multi-robot negotiation: The well-equipped robot can get more ample information than the poor-equipped robots (Wang et al. 2012)

Main contributions of this paper: "  Propose a new cloud-based architecture to achieve long-term autonomous localization potentially. "  Propose a new way to obtain the latest map information.

Background

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Outline Background 1

Cloud-based Localization 2

Conclusion 4

Experiments 3

Cloud-based Localization Architecture

Fig 1. Cloud-based localization Architecture

1. Offline phases: (1) Label a set of points along the roads in the selected area; (2) Extract the geodetic coordinate of these points from the Google Earth; (3) Update the road network map in the cloud.

Cloud-based Localization Algorithms

Fig 3. Updated road networks from the Google Earth

Fig 2. Road networks previously existing

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2. Online phases (1) Send the initial position estimated by the GPS to the cloud; (2) Pull the estimated initial position to the nearest road point as the initial position of robot (in the cloud); (3) Extract a local road network map within a certain distance around the initial position of the robot (in the cloud);

Cloud-based Localization Architecture

Fig 4. Extracted a local road network map

Fig 5. Robot path is segmented into a series of line segments

Fig 6. Geometric extraction of robot-terrain inclination model

(4) Compute the Robot-Terrain Inclination (RTI) model (in the cloud);

A. Motion Model:

B. Sensor Model:

(5) Send the RTI model to the robot;

(6) Achieve particle filter localization based on the RTI model (on the robot).

Outline Background 1

Cloud-based Localization 2

Conclusion 4

Experiments 3

Platform: Mobile robot(Summit XL); IMU(NAV 440) Scenario: Travel distance: 500 m Robot speed: 1.0 m/s

Experimental Setups

Fig 7. Experiment platform Fig 8. Google Earth and the point sets on the pre-planned path

Experimental Results

Fig 9. The estimation of the robot position by the proposed technique

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Experimental Results

Fig. 10 The position estimation errors of the robot using the proposed technique

Outline

Background 1

Cloud-based Localization 2

Conclusions 4

Experiments 3

"   Conclusions"   This paper introduces a cloud-based outsourcing localization technique for

a mobile robot on outdoor road networks.

"   Experimental results validate the proposed technique and illustrate that the

proposed technique has capability to achieve online localization.

"   Future works"   This method will be applied to more complex large-scale/long-term

circumstances.

1. H. Zhao et al., "SLAM in a Dynamic Large Outdoor Environment using a Laser Scanner," presented atInternational Conference on Robotics and Automation (ICRA), 19-23 May. 2008, Pasadena,USA, 2008.

2. N. Sunderhauf, P. Neubert, P. Protzel, "Predicting the change-A Step Towards Life-Long Operation in Everyday Environments," presented at 2013Robotics: Science and Systems (RSS) Robotics Challenges and Vision Workshop,14-18 May. 2012, Minnesota,USA, 2012. �

3. R. Arumugam et al., "DAvinCi: A Cloud Computing Framework for Service Robots," presented at International Conference on Robotics and Automation (ICRA), 3-8 May, Anchorage,USA, 2010.

4. B. Kehoe et al., "Cloud-Based Robot Grasping with the Google Object Recognition Engine," presented at International Conference on Robotics and Automation (ICRA), 6-10 May,

5. L.Wang, M. Liu and M. Meng, "Towards Cloud Robotic System: A Case Study of Online Co-localization for Fair resource Competence," presented at International Conference on Robotics and Biomimetics, 11-14 December, Guangzhou, China, 2012.Karlsruhe, Germany, 2013.

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Thank you! �