Abstract
Autonomous explorative robots frequently encounter scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either manoeuvring around the obstacles or moving towards various frontiers. In this work, we propose a 2 stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment which can be occupied in the future. The second stage is a Trajectory Sampling network which provides a finegrained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings. For the outdoor case, we use the KITTI dataset and our own outdoor driving dataset. In the indoor setting, we use an autonomous drone to navigate various scenarios and also a ground robot which can explore the environment using the trajectories proposed by our framework. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons portray the performance gain of the proposed architecture over a diverse set of methods against which it is compared.
Abstract (translated)
自主探索性机器人经常会遇到这样的场景，即可以追寻多个未来的轨迹。通常情况下，在障碍物周围有多条路径或朝向不同边界的轨迹选项。在这种情况下，人类可以固有地感知和推理周围的环境，以确定绕过障碍物或向各个边界移动的几种可能性。在这项工作中，我们提出了一个2阶段卷积神经网络架构，它模拟了这样一种能力，即将感知的环境映射到机器人可以选择穿过的多个轨迹。第一阶段是一个轨迹规划网络，它表明环境中的不同区域将来可能被占用。第二阶段是一个轨迹采样网络，它在轨迹建议网络提出的区域上提供一个细粒度的轨迹。我们在各种复杂的现实生活环境中评估我们的框架。对于室外情况，我们使用Kitti数据集和我们自己的室外驾驶数据集。在室内环境中，我们使用自主无人机来导航各种场景，也使用地面机器人来利用我们的框架提出的轨迹来探索环境。我们的实验表明，该框架能够对障碍物、开放区域进行语义理解，并识别出机器人可以穿越的各种轨迹。我们的比较描述了所提议的体系结构在与之比较的一组不同方法上的性能增益。
URL
https://arxiv.org/abs/1906.03584
