Jackal robot 7-class terrain dataset, vision and proprioception sensors
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The Jackal UGV, from Clearpath Robotics, was used as the data collecting platform. This skid-steer four-wheel-drive vehicle, shown in Fig.1a, comes with an onboard IMU, two DC motors with encoders that measure wheel angular speeds, and current sensors that measure motor current outputs. On each side of the robot, the front wheel and back wheel are jointed with a gearbox and so spin together at the same rate and direction. The IMU provided vehicle attitude measurements in terms of Euler angles, as well as linear acceleration and angular rate of the vehicle body in three Euclidean axes. Camera systems such as the RS D435i and RS T265 were mounted to an aluminum frame attached to the top of the robot platform. While the tracking camera T265, faced forward, the D435i depth camera was positioned and tilted in a way that the camera had a clear visual of the terrain patch. The patch size was ~"680 mm × 340 mm" with a look ahead distance of 150 mm relative to the chassis of the vehicle. The D435i served as a regular RGB camera for this study and the depth images reconstructed by the D435i were not included in any of the data sets. The T265 camera was used as a Visual-Inertia Odometry (VIO) solution that provided ego-motion estimations of the vehicle. For this study, six different sensor signals were used: 1) current feedback, 2) wheel encoder readings from each side of the vehicle, 3) 6 DoF VIO measurements from the T265, 4) three-axis linear acceleration, 5) attitude measurement from the IMU, and 6) RGB images taken by the D435i. In addition to the RGB images, all other sensor signals were used as proprioceptive features. Seven terrain classes were investigated, including asphalt, brick road, grass, gravel, pavement, sand, and coated floors.
本研究采用Clearpath Robotics公司的Jackal无人地面车（UGV）作为数据采集平台。如图1a所示，这款滑移转向四轮驱动车辆搭载车载惯性测量单元（IMU, Inertial Measurement Unit）、两台配备编码器的直流电机（用于检测车轮角速度），以及用于采集电机电流输出的电流传感器。车辆两侧的前轮与后轮均通过变速箱相连，因此二者转速与转向方向保持一致。IMU可输出车辆的欧拉角姿态测量数据，以及车身沿三个欧几里得轴的线加速度与角速度数据。RS D435i与RS T265等摄像头系统被安装于搭载在车辆平台顶部的铝制支架上。追踪摄像头T265朝向前方，而D435i深度摄像头则经过定位与倾角调整，可清晰采集地形采样区域的画面。该采样区域尺寸约为680毫米×340毫米，相对于车辆底盘的前瞻距离为150毫米。本研究中D435i仅作为普通RGB摄像头使用，其生成的深度图像未纳入任何数据集。T265摄像头被用作视觉惯性里程计（VIO, Visual-Inertial Odometry）方案，用于输出车辆的自运动估计数据。本次研究共使用六种传感器信号：1）电流反馈数据；2）车辆两侧的车轮编码器读数；3）T265输出的六自由度VIO测量数据；4）三轴线加速度数据；5）IMU提供的姿态测量数据；6）D435i采集的RGB图像。除RGB图像外，其余所有传感器信号均被用作本体感知特征。本次研究共涵盖七种地形类别：沥青路面、砖铺道路、草地、碎石路面、硬化铺装路面、沙地以及涂层地坪。
