cloud based localization for mobile robot in...
TRANSCRIPT
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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! �