--- license: cc-by-nc-nd-4.0 tags: - Autonomous Driving - Computer Vision --- # Open MARS Dataset ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/ooi8v0KOUhWYDbqbfLkVG.jpeg)
## Welcome to the tutorial of Open MARS Dataset! Our paper has been accepted on CVPR 2024 🎉🎉🎉 Checkout our [project website](https://ai4ce.github.io/MARS/) for demo videos. Codes to reproduce the videos are available in `/visualize` folder of our [github repo](https://github.com/ai4ce/MARS).
## Intro ### The MARS dataset is collected with a fleet of autonomous vehicles from [MayMobility](https://maymobility.com/). Our dataset uses the same structure as the [NuScenes](https://www.nuscenes.org/nuscenes) Dataset: - Multitraversal: each location is saved as one NuScenes object, and each traversal is one scene. - Multiagent: the whole set is a NuScenes object, and each multiagent encounter is one scene.
## Download Both Multiagent and Multitraversal subsets are now available for [download on huggingface](https://huggingface.co/datasets/ai4ce/MARS).
## Overview This tutorial explains how the NuScenes structure works in our dataset, including how you may access a scene and query its samples of sensor data. - [Devkit Initialization](#initialization) - [Multitraversal](#load-multitraversal) - [Multiagent](#load-multiagent) - [Scene](#scene) - [Sample](#sample) - [Sample Data](#sample-data) - [Sensor Names](#sensor-names) - [Camera](#camera-data) - [LiDAR](#lidar-data) - [IMU](#imu-data) - [Ego & Sensor Pose](#vehicle-and-sensor-pose) - [LiDAR-Image projection](#lidar-image-projection)
## Initialization First, install `nuscenes-devkit` following NuScenes's repo tutorial, [Devkit setup section](https://github.com/nutonomy/nuscenes-devkit?tab=readme-ov-file#devkit-setup). The easiest way is install via pip: ``` pip install nuscenes-devkit ``` Import NuScenes devkit: ``` from nuscenes.nuscenes import NuScenes ``` #### Load Multitraversal loading data of location 10: ``` # The "version" variable is the name of the folder holding all .json metadata tables. location = 10 nusc = NuScenes(version='v1.0', dataroot=f'/MARS_multitraversal/{location}', verbose=True) ``` #### Load Multiagent loading data for the full set: ``` nusc = NuScenes(version='v1.0', dataroot=f'/MARS_multiagent', verbose=True) ```
## Scene To see all scenes in one set (one location of the Multitraversal set, or the whole Multiagent set): ``` print(nusc.scene) ``` Output: ``` [{'token': '97hitl8ya1335v8zkixvsj3q69tgx801', 'nbr_samples': 611, 'first_sample_token': 'udrq868482482o88p9r2n8b86li7cfxx', 'last_sample_token': '7s5ogk8m9id7apixkqoh3rep0s9113xu', 'name': '2023_10_04_scene_3_maisy', 'intersection': 10, 'err_max': 20068.00981996727}, {'token': 'o858jv3a464383gk9mm8at71ai994d3n', 'nbr_samples': 542, 'first_sample_token': '933ho5988jo3hu848b54749x10gd7u14', 'last_sample_token': 'os54se39x1px2ve12x3r1b87e0d7l1gn', 'name': '2023_10_04_scene_4_maisy', 'intersection': 10, 'err_max': 23959.357933579337}, {'token': 'xv2jkx6m0o3t044bazyz9nwbe5d5i7yy', 'nbr_samples': 702, 'first_sample_token': '8rqb40c919d6n5cd553c3j01v178k28m', 'last_sample_token': 'skr79z433oyi6jljr4nx7ft8c42549nn', 'name': '2023_10_04_scene_6_mike', 'intersection': 10, 'err_max': 27593.048433048432}, {'token': '48e90c7dx401j97391g6549zmljbg0hk', 'nbr_samples': 702, 'first_sample_token': 'ui8631xb2in5la133319c5301wvx1fib', 'last_sample_token': 'xrns1rpma4p00hf39305ckol3p91x59w', 'name': '2023_10_04_scene_9_mike', 'intersection': 10, 'err_max': 24777.237891737892}, ... ] ``` The scenes can then be retrieved by indexing: ``` num_of_scenes = len(nusc.scene) my_scene = nusc.scene[0] # scene at index 0, which is the first scene of this location print(first_scene) ``` Output: ``` {'token': '97hitl8ya1335v8zkixvsj3q69tgx801', 'nbr_samples': 611, 'first_sample_token': 'udrq868482482o88p9r2n8b86li7cfxx', 'last_sample_token': '7s5ogk8m9id7apixkqoh3rep0s9113xu', 'name': '2023_10_04_scene_3_maisy', 'intersection': 10, 'err_max': 20068.00981996727} ``` - `nbr_samples`: number of samples (frames) of this scene. - `name`: name of the scene, including its date and name of the vehicle it is from (in this example, the data is from Oct. 4th 2023, vehicle maisy). - `intersection`: location index. - `err_max`: maximum time difference (in millisecond) between camera images of a same frame in this scene.
## Sample Get the first sample (frame) of one scene: ``` first_sample_token = my_scene['first_sample_token'] # get sample token my_sample = nusc.get('sample', first_sample_token) # get sample metadata print(my_sample) ``` Output: ``` {'token': 'udrq868482482o88p9r2n8b86li7cfxx', 'timestamp': 1696454482883182, 'prev': '', 'next': 'v15b2l4iaq1x0abxr45jn6bi08j72i01', 'scene_token': '97hitl8ya1335v8zkixvsj3q69tgx801', 'data': { 'CAM_FRONT_CENTER': 'q9e0pgk3wiot983g4ha8178zrnr37m50', 'CAM_FRONT_LEFT': 'c13nf903o913k30rrz33b0jq4f0z7y2d', 'CAM_FRONT_RIGHT': '67ydh75sam2dtk67r8m3bk07ba0lz3ib', 'CAM_BACK_CENTER': '1n09qfm9vw65xpohjqgji2g58459gfuq', 'CAM_SIDE_LEFT': '14up588181925s8bqe3pe44d60316ey0', 'CAM_SIDE_RIGHT': 'x95k7rvhmxkndcj8mc2821c1cs8d46y5', 'LIDAR_FRONT_CENTER': '13y90okaf208cqqy1v54z87cpv88k2qy', 'IMU_TOP': 'to711a9v6yltyvxn5653cth9w2o493z4' }, 'anns': []} ``` - `prev`: token of the previous sample. - `next`': token of the next sample. - `data`: dict of data tokens of this sample's sensor data. - `anns`: empty as we do not have annotation data at this moment.
## Sample Data ### Sensor Names Our sensor names are different from NuScenes' sensor names. It is important that you use the correct name when querying sensor data. Our sensor names are: ``` ['CAM_FRONT_CENTER', 'CAM_FRONT_LEFT', 'CAM_FRONT_RIGHT', 'CAM_BACK_CENTER', 'CAM_SIDE_LEFT', 'CAM_SIDE_RIGHT', 'LIDAR_FRONT_CENTER', 'IMU_TOP'] ``` --- ### Camera Data All image data are already undistorted. To load a piece data, we start with querying its `sample_data` dictionary object from the metadata: ``` sensor = 'CAM_FRONT_CENTER' sample_data_token = my_sample['data'][sensor] FC_data = nusc.get('sample_data', sample_data_token) print(FC_data) ``` Output: ``` {'token': 'q9e0pgk3wiot983g4ha8178zrnr37m50', 'sample_token': 'udrq868482482o88p9r2n8b86li7cfxx', 'ego_pose_token': 'q9e0pgk3wiot983g4ha8178zrnr37m50', 'calibrated_sensor_token': 'r5491t78vlex3qii8gyh3vjp0avkrj47', 'timestamp': 1696454482897062, 'fileformat': 'jpg', 'is_key_frame': True, 'height': 464, 'width': 720, 'filename': 'sweeps/CAM_FRONT_CENTER/1696454482897062.jpg', 'prev': '', 'next': '33r4265w297khyvqe033sl2r6m5iylcr', 'sensor_modality': 'camera', 'channel': 'CAM_FRONT_CENTER'} ``` - `ego_pose_token`: token of vehicle ego pose at the time of this sample. - `calibrated_sensor_token`: token of sensor calibration information (e.g. distortion coefficient, camera intrinsics, sensor pose & location relative to vehicle, etc.). - `is_key_frame`: disregard; all images have been marked as key frame in our dataset. - `height`: image height in pixel - `width`: image width in pixel - `filename`: image directory relative to the dataset's root folder - `prev`: previous data token for this sensor - `next`: next data token for this sensor After getting the `sample_data` dictionary, Use NuScenes devkit's `get_sample_data()` function to retrieve the data's absolute path. Then you may now load the image in any ways you'd like. Here's an example using `cv2`: ``` import cv2 data_path, boxes, camera_intrinsic = nusc.get_sample_data(sample_data_token) img = cv2.imread(data_path) cv2.imshow('fc_img', img) cv2.waitKey() ``` Output: ``` ('{$dataset_root}/MARS_multitraversal/10/sweeps/CAM_FRONT_CENTER/1696454482897062.jpg', [], array([[661.094568 , 0. , 370.6625195], [ 0. , 657.7004865, 209.509716 ], [ 0. , 0. , 1. ]])) ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/EBo7WeD9JV1asBfbONTym.png) --- ### LiDAR Data Impoirt data calss "LidarPointCloud" from NuScenes devkit for convenient lidar pcd loading and manipulation. The `.bcd.bin` LiDAR data in our dataset has 5 dimensions: [ x || y || z || intensity || ring ]. The 5-dimensional data array is in `pcd.points`. Below is an example of visualizing the pcd with Open3d interactive visualizer. ``` import open3d as o3d from nuscenes.utils.data_classes import LidarPointCloud sensor = 'LIDAR_FRONT_CENTER' sample_data_token = my_sample['data'][sensor] lidar_data = nusc.get('sample_data', sample_data_token) data_path, boxes, _ = nusc.get_sample_data(my_sample['data'][sensor]) pcd = LidarPointCloud.from_file(data_path) print(pcd.points) pts = pcd.points[:3].T # open3d visualizer vis1 = o3d.visualization.Visualizer() vis1.create_window( window_name='pcd viewer', width=256 * 4, height=256 * 4, left=480, top=270) vis1.get_render_option().background_color = [0, 0, 0] vis1.get_render_option().point_size = 1 vis1.get_render_option().show_coordinate_frame = True o3d_pcd = o3d.geometry.PointCloud() o3d_pcd.points = o3d.utility.Vector3dVector(pts) vis1.add_geometry(o3d_pcd) while True: vis1.update_geometry(o3d_pcd) vis1.poll_events() vis1.update_renderer() time.sleep(0.005) ``` Output: ``` 5-d lidar data: [[ 3.7755847e+00 5.0539265e+00 5.4277039e+00 ... 3.1050100e+00 3.4012783e+00 3.7089713e+00] [-6.3800979e+00 -7.9569578e+00 -7.9752398e+00 ... -7.9960880e+00 -7.9981585e+00 -8.0107889e+00] [-1.5409404e+00 -3.2752687e-01 5.7313687e-01 ... 5.5921113e-01 -7.5427920e-01 6.6252775e-02] [ 9.0000000e+00 1.6000000e+01 1.4000000e+01 ... 1.1000000e+01 1.8000000e+01 1.6000000e+01] [ 4.0000000e+00 5.3000000e+01 1.0200000e+02 ... 1.0500000e+02 2.6000000e+01 7.5000000e+01]] ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/ZED1ba3r7qeBzkeNQK3oq.png) --- ### IMU Data IMU data in our dataset is saved as json files. ``` sensor = 'IMU_TOP' sample_data_token = my_sample['data'][sensor] lidar_data = nusc.get('sample_data', sample_data_token) data_path, boxes, _ = nusc.get_sample_data(my_sample['data'][sensor]) imu_data = json.load(open(data_path)) print(imu_data) ``` Output: ``` {'utime': 1696454482879084, 'lat': 42.28098291158676, 'lon': -83.74725341796875, 'elev': 259.40500593185425, 'vel': [0.19750464521348476, -4.99952995654127e-27, -0.00017731071625348704], 'avel': [-0.0007668623868539726, -0.0006575787383553688, 0.0007131154834496556], 'acc': [-0.28270150907337666, -0.03748669268679805, 9.785771369934082]} ``` - `lat`: GPS latitude. - `lon`: GPS longitude. - `elev`: GPS elevation. - `vel`: vehicle instant velocity [x, y, z] in m/s. - `avel`: vehicle instant angular velocity [x, y, z] in rad/s. - `acc`: vehicle instant acceleration [x, y, z] in m/s^2. --- ### Vehicle and Sensor Pose Poses are represented as one rotation matrix and one translation matrix. - rotation: quaternion [w, x, y, z] - translation: [x, y, z] in meters Sensor-to-vehicle poses may differ for different vehicles. But for each vehicle, its sensor poses should remain unchanged across all scenes & samples. Vehicle ego pose can be quaried from sensor data. It should be the same for all sensors in the same sample. ``` # get the vehicle ego pose at the time of this FC_data vehicle_pose_fc = nusc.get('ego_pose', FC_data['ego_pose_token']) print("vehicle pose: \n", vehicle_pose_fc, "\n") # get the vehicle ego pose at the time of this lidar_data, should be the same as that queried from FC_data as they are from the same sample. vehicle_pose = nusc.get('ego_pose', lidar_data['ego_pose_token']) print("vehicle pose: \n", vehicle_pose, "\n") # get camera pose relative to vehicle at the time of this sample fc_pose = nusc.get('calibrated_sensor', FC_data['calibrated_sensor_token']) print("CAM_FRONT_CENTER pose: \n", fc_pose, "\n") # get lidar pose relative to vehicle at the time of this sample lidar_pose = nusc.get('calibrated_sensor', lidar_data['calibrated_sensor_token']) print("CAM_FRONT_CENTER pose: \n", lidar_pose) ``` Output: ``` vehicle pose: {'token': 'q9e0pgk3wiot983g4ha8178zrnr37m50', 'timestamp': 1696454482883182, 'rotation': [-0.7174290249840286, 0.0, -0.0, -0.6966316057361065], 'translation': [-146.83352790433003, -21.327001411798392, 0.0]} vehicle pose: {'token': '13y90okaf208cqqy1v54z87cpv88k2qy', 'timestamp': 1696454482883182, 'rotation': [-0.7174290249840286, 0.0, -0.0, -0.6966316057361065], 'translation': [-146.83352790433003, -21.327001411798392, 0.0]} CAM_FRONT_CENTER pose: {'token': 'r5491t78vlex3qii8gyh3vjp0avkrj47', 'sensor_token': '1gk062vf442xsn86xo152qw92596k8b9', 'translation': [2.24715, 0.0, 1.4725], 'rotation': [0.49834929780875276, -0.4844970241435727, 0.5050790448056688, -0.5116695901338464], 'camera_intrinsic': [[661.094568, 0.0, 370.6625195], [0.0, 657.7004865, 209.509716], [0.0, 0.0, 1.0]], 'distortion_coefficient': [0.122235, -1.055498, 2.795589, -2.639154]} CAM_FRONT_CENTER pose: {'token': '6f367iy1b5c97e8gu614n63jg1f5os19', 'sensor_token': 'myfmnd47g91ijn0a7481eymfk253iwy9', 'translation': [2.12778, 0.0, 1.57], 'rotation': [0.9997984797097376, 0.009068089160690487, 0.006271772522201215, -0.016776012592418482]} ```
## LiDAR-Image projection - Use NuScenes devkit's `render_pointcloud_in_image()` method. - The first variable is a sample token. - Use `camera_channel` to specify the camera name you'd like to project the poiint cloud onto. ``` nusc.render_pointcloud_in_image(my_sample['token'], pointsensor_channel='LIDAR_FRONT_CENTER', camera_channel='CAM_FRONT_CENTER', render_intensity=False, show_lidarseg=False) ``` Output: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/zDrqBzfs6oV5ugVCsCQLL.png)