We present a novel end-to-end diffusion-based trajectory generation method, DTG, for mapless global navigation in challenging outdoor scenarios with occlusions and unstructured off-road features like grass, buildings, bushes, etc. Given a distant goal, our approach computes a trajectory that satisfies the following goals: (1) minimize the travel distance to the goal; (2) maximize the traversability by choosing paths that do not lie in undesirable areas. Specifically, we present a novel Conditional RNN(CRNN) for diffusion models to efficiently generate trajectories. Furthermore, we propose an adaptive training method that ensures that the diffusion model generates more traversable trajectories. We evaluate our methods in various outdoor scenes and compare the performance with other global navigation algorithms on a Husky robot. In practice, we observe at least a 15% improvement in traveling distance and around a 7% improvement in traversability.

翻译：我们提出了一种新颖的端到端基于扩散的轨迹生成方法DTG，用于在具有遮挡和草地、建筑物、灌木丛等非结构化越野特征的挑战性室外场景中实现无地图全局导航。给定一个远程目标，我们的方法计算出的轨迹需满足以下目标：(1) 最小化到达目标的行驶距离；(2) 通过选择避开不良区域的路径来最大化可通行性。具体而言，我们提出了一种用于扩散模型的条件循环神经网络（Conditional RNN, CRNN），以高效生成轨迹。此外，我们还提出了一种自适应训练方法，确保扩散模型生成更具可通行性的轨迹。我们在各种户外场景中评估了该方法，并在Husky机器人上与其他全局导航算法进行了性能对比。实际测试中，我们观察到行驶距离至少改善了15%，可通行性提升了约7%。