Cloud Based Localization for Mobile Robot in Outdoors

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1 11/12/13 15:11 Cloud Based Localization for Mobile Robot in Outdoors Xiaorui Zhu (presenter), Chunxin Qiu, Yulong Tao, Qi Jin Harbin Institute of Technology Shenzhen Graduate School, China

2 11/12/13 15:11 Outline 1 Background 2 Cloud-based Localization 3 Experiments 4 Conclusion

3 Background 11/12/13 15:11 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

unavailable in some cases Laser scanner Advantage: high precision Disadvantage: time-consuming

4 11/12/13 15:11 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)

Cloud robotics: Offload computation: SLAM: feature extraction and filter for the state estimation (Arumugam et al.

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

longterm autonomous localization potentially.

6 11/12/13 15:11 Outline 1 Background 2 Cloud-based Localization 3 Experiments 4 Conclusion

7 Cloud-based Localization Architecture Fig 1. Cloud-based localization Architecture

8 Cloud-based Localization Algorithms 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. Fig 2. Road networks previously existing Fig 3. Updated road networks from the Google Earth

(2) Extract the geodetic coordinate of these points from the Google Earth; (3)

9 11/12/13 15:11 Cloud-based Localization Architecture 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); Fig 4. Extracted a local road network map

initial position to the nearest road point as the initial position of robot (in the cloud); (3)

10 (4) Compute the Robot-Terrain Inclination (RTI) model (in the cloud); Fig 5. Robot path is segmented into a series of line segments Fig 6. Geometric extraction of robot -terrain inclination model

11 (5) Send the RTI model to the robot; (6) Achieve particle filter localization based on the RTI model (on the robot). A. Motion Model: B. Sensor Model:

12 Outline 1 Background 2 Cloud-based Localization 3 Experiments 4 Conclusion

13 Experimental Setups Platform: Scenario: Mobile robot(summit XL); IMU(NAV 440) Travel distance: 500 m Robot speed: 1.0 m/s Fig 7. Experiment platform Fig 8. Google Earth and the point sets on the pre-planned path

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

15 Experimental Results 11/12/13 15:11 Fig. 10 The position estimation errors of the robot using the proposed technique

16 Outline 1 Background 2 Cloud-based Localization 3 Experiments 4 Conclusions

17 " 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.

" Experimental results validate the proposed technique and illustrate that the proposed

18 1. H. Zhao et al., "SLAM in a Dynamic Large Outdoor Environment using a Laser Scanner," presented atinternational Conference on Robotics and Automation (ICRA), May. 2008, Pasadena,USA, 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, May. 2012, Minnesota,USA, 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, 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, December, Guangzhou, China, 2012.Karlsruhe, Germany, 2013.

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.

19 Thank you! 11/12/13 15:11

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