This paper contributes a novel learning-based method for aggressive task-driven compression of depth images and their encoding as images tailored to collision prediction for robotic systems. A novel 3D image processing methodology is proposed that accounts for the robot's size in order to appropriately "inflate" the obstacles represented in the depth image and thus obtain the distance that can be traversed by the robot in a collision-free manner along any given ray within the camera frustum. Such depth-and-collision image pairs are used to train a neural network that follows the architecture of Variational Autoencoders to compress-and-transform the information in the original depth image to derive a latent representation that encodes the collision information for the given depth image. We compare our proposed task-driven encoding method with classical task-agnostic methods and demonstrate superior performance for the task of collision image prediction from extremely low-dimensional latent spaces. A set of comparative studies show that the proposed approach is capable of encoding depth image-and-collision image tuples from complex scenes with thin obstacles at long distances better than the classical methods at compression ratios as high as 4050:1.

该研究提出了一种新的基于学习的方法，用于对深度图像进行积极的任务驱动压缩，并将其编码为适用于机器人系统碰撞预测的图像。通过提出一种新颖的3D图像处理方法，结合了机器人的尺寸，使得深度图像中表示的障碍物得到适当的“膨胀”，从而获得机器人在摄像机视锥内沿任意给定射线可行碰撞避免方式的距离。利用这些密切相关的深度和碰撞图像对训练依据变分自动编码器架构的神经网络进行压缩和转换，以获得编码给定深度图像的碰撞信息的潜在表示。通过与经典的任务非特定方法进行比较，我们证明了我们提出的任务驱动编码方法在极低维潜在空间中用于碰撞图像预测任务时显示出更出色的性能。一系列的比较研究表明，该方法能够在高达4050:1的压缩比下更好地编码来自具有长距离的复杂场景中的薄障碍物的深度图像和碰撞图像对。

基于深度图像的任务驱动碰撞编码压缩