Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry
Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry
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DOI：
10.1109/lra.2019.2894849
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发表时间：
2019-04-01
影响因子：
5.2
通讯作者：
Giusti, Alessandro
中科院分区：
文献类型：
--
作者：
Nava, Mirko;Guzzi, Jerome;Giusti, Alessandro
我们引入了一种通用的自监督方法，根据远程传感器（例如相机）的当前输出来预测短程传感器（例如接近传感器）的未来输出。我们假设前者与要感知的某些信息直接相关（例如给定位置存在障碍物），而后者信息丰富但难以直接解释。我们在小型移动机器人上实例化并实现了该方法，通过在自动获取的数据集上训练卷积神经网络，使用机器人前向摄像头的视频流检测不同距离的障碍物。我们定量评估对未见过的场景的预测质量，定性评估对不同操作条件的鲁棒性，并演示作为避障控制器的唯一输入的使用。我们还在具有互补特征的不同模拟场景中实例化了该方法，以例证我们贡献的普遍性。
We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.