pointcloud_to_rangeimage.yaml

point_cloud_to_rangeimage:
    # Velodyne sensor setup
    vlp_rpm: 600.0
    num_layers: 32
    firing_cycle: 0.000055296
    # Points are ordered with ascending elevation
    elevation_offsets: [-25, -15.639, -11.31, -8.843, -7.254, -6.148, -5.333, -4.667, -4, -3.667, -3.333, -3, -2.667, -2.333, -2, -1.667, -1.333, -1, -0.667, -0.333,
    0, 0.333, 0.667, 1, 1.333, 1.667, 2.333, 3.333, 4.667, 7, 10.333, 15]
    azimuth_offsets: [1.4, -1.4, 1.4, -1.4, 1.4, -1.4, 4.2, 1.4, -1.4, -4.2, 4.2, 1.4, -1.4, -4.2, 4.2, 1.4, -1.4, -4.2, 4.2, 1.4,
    -1.4, -4.2, 4.2, 1.4, -1.4, -4.2, 1.4, -1.4, 1.4, -1.4, 1.4, -1.4]

    #Maximum range of sensor
    threshold : 200

    # Set to true to record point cloud image dataset
    record_images: false
    # Path to store lidar compression dataset. The path should contain three sub-folders named azimuth, range and intensity.
    record_path: /catkin_ws/images/


## Method image compression.
# image_compression for for jpeg or png compression
# or
# one of the RNN based methods:
# additive_lstm
# oneshot_lstm with one-shot reconstruction
# additive_gru for GRU with additive reconstruction
compression_method: additive_lstm


rnn_compression:
    # weights path of RNN image compression model
    weights_path: /catkin_ws/models/additive_lstm_32b_32iter.hdf5

    # Bottleneck size of the model for RNN models
    bottleneck: 32

    # Number of iterations for compression of RNN models
    # Fewer number of iterations leads to smaller compressed data size and lower compression quality.
    num_iters: 32

    xla: True
    mixed_precision: False


image_compression:
    # Parameters for compression of range image using jpeg or png compression
    image_compression_method: jpeg  # png or jpeg
    show_debug_prints: false