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local_mapping_lidar's Introduction

local_mapping_lidar

Local Mapping algorithm for LiDAR sensor and monocular camera.

The algorithm is as follows:

  • The ground plane is removed from the LiDAR point cloud using RANSAC.
  • The outlier points (not on the ground plane) are clustered using DBSCAN.
  • On the camera image, a bounding box is cropped for each detected cone of the point cloud,, by knowing the LiDAR resolution and the camera model.
  • The cone is classified by its class (or as a "not cone") by a simple convolutional neural network.
  • For each cone classified, associate its class to the previous detected position.

Inputs

  • /pointcloud (sensor_msgs/PointCloud2)
  • /image (sensor_msgs/Image)
  • /camera_info (sensor_msgs/CameraInfo)

Outputs

  • /cones (lart_msgs/ConeArray)

Required transforms

  • base_link -> lidar_link
  • base_link -> camera_link

Dependencies

  • ROS Humble
  • CMake
  • C++ compiler
  • Open3D
  • LibTorch
  • lart_msgs

local_mapping_lidar's People

Contributors

carlostojal avatar

local_mapping_lidar's Issues

Implement the first iteration of the LiDAR mapper

Implement the first iteration of the LiDAR local mapper. This is a simple implementation in Python using Open3D to check the potential of the concept.
This implementation should be as simple as:

  • Remove the ground plane using RANSAC
  • Create clusters for the cones using DBSCAN

Later on, a monocular camera can be added to classify the cones detected in the point cloud using a simple CNN, but not the scope at this stage.

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