newer_college.yaml

# Enable debug mode will disable multi-processor so that you can visualize the inputs in order
name: "newer_college"
debug_mode: False
hardware_synced: False

sensors_info:
  # https://ori-drs.github.io/newer-college-dataset/platform/
  camera:
    frequency: 30 # Hz
    type: "Intel RealSense D435i"
    shutter: "global"
    shape: [480, 848] # h 480 , w 848
  lidar:
    frequency: 10 # Hz
    type: "Ouster OS-1-64"
    beams: 64
    range: 120 # m
    fov: [45, 360] # vertical, horizontal

calibrations:
  # Below are extracted from kilbra calibration yaml
  lidar_to_lidar_imu_hom: [[ -1.0000000,  0.0000000,  0.0000000, -0.006253 ],
                         [ 0.0000000,  -1.0000000,  0.0000000, 0.011775 ],
                         [ 0.0000000,  0.0000000,  1.0000000 , -0.028535 ],
                         [ 0.0000000,  0.0000000,  0.0000000 , 1.0000000 ]]
  lidar_imu_to_cam_hom: [ [ 0.7099216283860768, -0.7041416662469333, 0.013992691115502887, 0.015627525086609025 ],
                      [ 0.02460002682200807, 0.004936229792296243, -0.9996851866041602, -0.01981648043281047 ],
                      [ 0.703850921879071, 0.7100423561242176, 0.020826240168402843, -0.07544143005003269 ],
                      [ 0.0, 0.0, 0.0, 1.0 ] ]
  R_hom: [[ 431.3873911369959, 0., 430.2496176152663 ],
      [ 0., 427.4407802012019, 238.52694867508183 ],
      [ 0., 0., 1. ]]

  P: [[ 0.007017508922248575, -0.002598448110748391, -0.0014755213248257705, 0.0006640775125219394]]

raw_data:
  name: short_time_experiment
  root_dir: "Replace this with your raw data folder"
  camera_sub_folder: infra1
  lidar_sub_folder: ouster_scan
  time_offsets_csv_fp: "Replace this with your timeoffset csv path" # timeoffset/nc-short-time-offsets.csv
  generated_fp:
    synced_raw_data_info: "Replace this with where you would like to save the synced raw data info" # xxxx/synced_raw_data_info.pkl

# This section is only used to generate annotation data, optimizing better visualization
labelling_data:
  chunk_size: 500
  downsample_ratio: 0.1 # TODO: This depends on how big the machine is
  output_format: 'jpg'
  output_dir: "Replace this with where you would like to save the annotation targets"
  size:
    H: 300
    W: 500

training_data:
  chunk_size: 2048
  downsample_ratio: 0.5 # TODO: This depends on how big the machine is
  skip_frames: 400 # the first 400 frames are just staying still, meaningless. So I just skip them
  z_norm_methods: 'lidar_range'
  output_format: 'tfrecords'
  output_dir: "Replace this with where you would like to save the tfrecords"
  split_ratio: [0.7, 0.2, 0.1] # training/validation/testing
  sampling_stride: 1
  sampling_window: 5
  crop_shape: [[0, 92], [16, 144]] # Cropping the H, W of X so that it's 2***
  features:
    X:
      modality: 'image'
      data_type: 'float32'
      nhwc: True
      H: 64
      W: 128
      C: 24
      feature_name:  'X'
    Y:
      modality: 'scalar'
      data_type: 'float32'
      feature_name: 'Y'