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object detection, shape fitting, and tracking in lidar point cloud data

object detection is a technique that identifies and locates objects in a scene. this enables you to detect 3-d objects in a point cloud. lidar toolbox™ includes functionality that enables you to detect objects using geometric shape fitting or deep learning with convolutional neural networks.

  • geometric shape fitting — detect the 3-d geometry of the objects in the point cloud by using ground segmentation and plane-fitting algorithms. you can detect the location, dimensions and direction of each object. you can use the detected objects for downstream workflows such as tracking, path planning and labeling.

  • deep learning — a deep learning approach to object detection uses convolutional neural networks to perform object detection. lidar toolbox includes object detection workflows that use neural networks such as pointpillars and complex-yolo v4. you can train a custom object detection model, or use the available pretrained networks and further tune it for your application. the toolbox also supports cuda® mex code generation for pointpillars and squeezesegv2 networks.

object tracking is a technique that estimates and tracks the movement of objects across multiple scans of a scene. object tracking consists of assigning a unique id to detected objects and tracking their movement across point cloud frames. lidar toolbox includes detection and tracking workflows for vehicles, road lanes, and curbs. most of these workflows use the joint probabilistic data association (jpda) tracker.

deep learning-based object detection in lidar point clouds.

functions

shape fitting

fit cuboid over point cloud
fit plane to 3-d point cloud

geometric models

object for storing parametric plane model
parametric cuboid model

load training data

ground truth label data
combine data from multiple datastores
datastore with custom file reader
datastore for bounding box label data

augment and preprocess training data

create randomized 3-d affine transformation
apply geometric transformation to bounding boxes
transform 3-d point cloud

object detection

pointpillars object detector
train pointpillars object detector
detect objects using pointpillars object detector
detect loam feature points from 3-d lidar data
create training data for lidar object detection

visualize results

display shapes on image, video, or point cloud
plot 3-d point cloud

evaluate results

evaluate average orientation similarity metric for object detection
compute bounding box overlap ratio

topics


  • learn point cloud processing using deep learning.


  • define pointpillars network and learn how to perform object detection using the same.

  • (deep learning toolbox)

    learn how to use datastores in deep learning applications.

  • (deep learning toolbox)

    discover all the deep learning layers in matlab®.

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