Some problems about sensor coord system

Nobody has claimed this yet.

I try like this:
```
my_sample = level5data.get('sample', my_sample_token)
sensor_channel_list=['CAM_FRONT', 'LIDAR_TOP', 'CAM_BACK']
for sensor_channel in sensor_channel_list:
my_sample_data = level5data.get('sample_data', my_sample['data'][sensor_channel])
Path, box_list, cam_intrinsic = level5data.get_sample_data(my_sample_data['token'])
print('{} channel path = {}'.format(sensor_channel, Path))
if any(box_list):
print('{} channel box = {}'.format(sensor_channel, box_list[0]))
```
I get the result like this:
```
CAM_FRONT channel path = J:\data\lyft\nuscenes-devkit\notebooks\..\..\perception\one_scene\train_images\host-a101_cam0_1241893239450000006.jpeg
CAM_FRONT channel box = label: nan, score: nan, xyz: [-17.98, 1.52, 27.38], wlh: [1.70, 3.89, 1.49], rot axis: [0.01, -0.70, 0.72], ang(degrees): 178.16, ang(rad): 3.11, vel: nan, nan, nan, name: car, token: 9f1434fa2688e89f7770958b13a24cb6b1d815a31ab1efb6346565799482d350
LIDAR_TOP channel path = J:\data\lyft\nuscenes-devkit\notebooks\..\..\perception\one_scene\train_lidar\host-a101_lidar1_1241893239502712366.bin
LIDAR_TOP channel box = label: nan, score: nan, xyz: [27.67, -16.85, -0.47], wlh: [1.55, 4.05, 1.41], rot axis: [-0.02, 0.03, 1.00], ang(degrees): -39.19, ang(rad): -0.68, vel: nan, nan, nan, name: car, token: fdfa2d9494bd4b2bc183522bc8a71ee02e0eeb451dfb5f91d057ffcdb8a8e7fc
CAM_BACK channel path = J:\data\lyft\nuscenes-devkit\notebooks\..\..\perception\one_scene\train_images\host-a101_cam3_1241893239500000006.jpeg
CAM_BACK channel box = label: nan, score: nan, xyz: [17.29, 0.37, 27.02], wlh: [1.55, 4.05, 1.41], rot axis: [0.84, -0.39, 0.38], ang(degrees): 101.59, ang(rad): 1.77, vel: nan, nan, nan, name: car, token: fdfa2d9494bd4b2bc183522bc8a71ee02e0eeb451dfb5f91d057ffcdb8a8e7fc
```
I feel confused about the xyz of the box_list[0], so I look into the source code.
```
box_list = []
for box in boxes:
if flat_vehicle_coordinates:
# Move box to ego vehicle coord system parallel to world z plane
ypr = Quaternion(pose_record["rotation"]).yaw_pitch_roll
yaw = ypr[0]

box.translate(-np.array(pose_record["translation"]))
box.rotate_around_origin(Quaternion(scalar=np.cos(yaw / 2), vector=[0, 0, np.sin(yaw / 2)]).inverse)

else:
# Move box to ego vehicle coord system
box.translate(-np.array(pose_record["translation"]))
box.rotate_around_origin(Quaternion(pose_record["rotation"]).inverse)

# Move box to sensor coord system
box.translate(-np.array(cs_record["translation"]))
box.rotate_around_origin(Quaternion(cs_record["rotation"]).inverse)

if sensor_record["modality"] == "camera" and not box_in_image(
box, cam_intrinsic, image_size, vis_level=box_vis_level
):
continue

box_list.append(box)
```
The parameter flat_vehicle_coordinates is False, so I think the final box_list‘s xyz is in sensor coord system.
But the result I find the z is 27.38 or 27.02 (m ？)
I want to know where to find the definition of each sensor coord system ? I'm confused about the result, especially the z axis.

Thanks for your contribution for this big dataset very much !

Start with the get_sample_data call and the shown coordinate-transform block, then compare the roles of pose_record, cs_record, sensor_record, and cam_intrinsic. Done means the coordinate-system definitions and the meaning and units of returned box xyz values, especially z, are explicitly documented for each sensor channel.

Written by the indexing model from the issue text.