Occupancy Grid based Urban Localization Using Weighted Point Cloud - - PowerPoint PPT Presentation

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Jan 13, 2023 365 likes •531 views

Occupancy Grid based Urban Localization Using Weighted Point Cloud Lindong Guo, Ming Yang, Bing Wang and Chunxiang Wang CyberC3 Intelligent Vehicle Labs Shanghai Jiao Tong University cyberc3.sjtu.edu.cn ITSC 2016 PPNIV Contents Motivation

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Occupancy Grid based Urban Localization Using Weighted Point Cloud

Lindong Guo, Ming Yang, Bing Wang and Chunxiang Wang CyberC3 Intelligent Vehicle Labs Shanghai Jiao Tong University cyberc3.sjtu.edu.cn ITSC 2016 PPNIV

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Contents Motivation & Our goal System architecture Sensor data process

Weighted point cloud
Occupancy grid generation

Localization Experiments Conclusion

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Motivation Localization

Without map
GNSS based methods
SLAM based methods
Using prebuilt map
Accuracy
Cost

Environment

Urban，structured
Relatively large scale

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Our goal Suitable map representation

2D/2.5D representation
Light and flexible

Reasonable sensor configuration

Relatively low-cost
General or universal

Light localization method

Matching with generated map, not raw data

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System architecture

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Weighting

Ring Compression Analysis based curb detection method

[A. Y. Hata，IV2014]

Based on plane model
curbs
building facades
Useful features

Weighted point cloud

Featured point cloud extraction Point cloud weighting

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Weighted point cloud Example of weighted point cloud

Filter
Road surface
Disadvantages
rise and fall of roads
Moving objects
Other disturbs
Need further filtering

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Occupancy grid map generation Properties for each grid

Height gap
Points account
Points weight
Function

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Localization Pose initialization

Gps added
Pyramid map matching

Localization with maximum likelihood matching

SLIDE 10

Experimental results Testing platform

CyberTiggo
VLP-16 or LMS-151 on top
Odometer
Core-i7＠２．６ＧＨｚ

3D 2D

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Experimental results Experimental fields

2 scenes
Long distance

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Experimental results Results

I II

SLIDE 13

demo Robot with hokuyo Vehicle with single layer laser scanner Vehicle with 3D lidar

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Conclusion Conclusion

Relatively accurate (but not enough)
Light weight
Adaptive approach
Testing and working on products

Future work

Extend the method, make it general
Combine with SLAM
Moving objects while mapping and localization
more…

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