thesis.txt

1. vehicle detection from 3D Lidar Using Fully Convolutional Network

Lidar Processing
1. project range scans as 2D maps similat to the depth map of RGBD data
  1. velodyne scan can be roughly projected and discretized into a 2D point map.
  θ = atan2(y, x)                     # azimuth angle
  φ = arcsin(z / √x**2 + y**2 + z**2) # elevation angle
  r = int(θ / ⊿θ)                    # 2D Map Position
  c = int(φ / ⊿φ)                    # 2D Map Position
  ⊿θ is the average horizontal and vertical angle resolution between consecutive beam emitters
  ⊿θ = 0.08 http://velodynelidar.com/hdl-64e.html
  ⊿φ = 0.4

  each (r, c) have 2-channel data (d, z), d is √x**2 + y**2



kitti

Label
#Values    Name      Description
----------------------------------------------------------------------------
   1    type         Describes the type of object: 'Car', 'Van', 'Truck',
                     'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',
                     'Misc' or 'DontCare'
   1    truncated    Float from 0 (non-truncated) to 1 (truncated), where
                     truncated refers to the object leaving image boundaries
   1    occluded     Integer (0,1,2,3) indicating occlusion state:
                     0 = fully visible, 1 = partly occluded
                     2 = largely occluded, 3 = unknown
   1    alpha        Observation angle of object, ranging [-pi..pi]
   4    bbox         2D bounding box of object in the image (0-based index):
                     contains left, top, right, bottom pixel coordinates
   3    dimensions   3D object dimensions: height, width, length (in meters)
   3    location     3D object location x,y,z in camera coordinates (in meters)
   1    rotation_y   Rotation ry around Y-axis in camera coordinates [-pi..pi]
   1    score        Only for results: Float, indicating confidence in
                     detection, needed for p/r curves, higher is better.
type trun occl alpha bbox  bbox   bbox  bbox  dimen dimen dimen loc   loc loc  rotate
Car 0.89   0   2.29   0.00 194.70 414.71 373.00 1.57 1.67 4.14 -2.75 1.70 4.10 1.72