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SLAM with occupancy grid and particle filter, using lidar, joints, IMU and odometry data from THOR humanoid robot

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MaxLing/particle_filter_SLAM

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ESE650_2018_Project4.pdf

ESE650_2018_Project4.pdf

 
 

README.md

README.md

 
 

SLAM.py

SLAM.py

 
 

SLAM_functions.py

SLAM_functions.py

 
 

load_data.py

load_data.py

 
 

wudao_project4.pdf

wudao_project4.pdf

 
 

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Upenn ESE650 Learning in Robotics, Project 4

  1. Put lidar and joint data .m file into folder .\train\data
  2. Modify data_idx and dir in SLAM.py
  3. Run SLAM.py, the tunning on map and particles can be done in init_SLAM()
  4. a sequence of map plot will be shown, the last plot will be saved
    occupied grid is in black, free grid is in white, undetermined is in gray, robot trajectory in blue and lidar scan of the best particle in green train2 test

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SLAM with occupancy grid and particle filter, using lidar, joints, IMU and odometry data from THOR humanoid robot

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particle-filter slam occupancy-grid-map ese650

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