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.

本文提出了一个基于使用卷积神经网络 (CNN) 架构的两阶段方法，实现了模拟人类感知环境的能力，将周围环境映射到机器人可选择遍历的多条轨迹上。第一阶段是轨迹建议网络，建议出环境中可以在未来占用的各种区域。第二阶段是轨迹采样网络，通过轨迹建议网络提出的区域提供精细的轨迹。作者在各种复杂真实场景中评估了该框架，并将其性能与其他多种方法进行了比较，结果表明该框架能够改善机器人导航任务的性能。