In the realm of autonomous mobile robots, safe navigation through unpaved outdoor environments remains a challenging task. Due to the high-dimensional nature of sensor data, extracting relevant information becomes a complex problem, which hinders adequate perception and path planning. Previous works have shown promising performances in extracting global features from full-sized images. However, they often face challenges in capturing essential local information. In this paper, we propose Crop-LSTM, which iteratively takes cropped image patches around the current robot's position and predicts the future position, orientation, and bumpiness. Our method performs local feature extraction by paying attention to corresponding image patches along the predicted robot trajectory in the 2D image plane. This enables more accurate predictions of the robot's future trajectory. With our wheeled mobile robot platform Raicart, we demonstrated the effectiveness of Crop-LSTM for point-goal navigation in an unpaved outdoor environment. Our method enabled safe and robust navigation using RGBD images in challenging unpaved outdoor terrains. The summary video is available at https://youtu.be/iIGNZ8ignk0.

翻译：在自主移动机器人领域，在非铺装户外环境中安全导航仍然是一项具有挑战性的任务。由于传感器数据的高维特性，提取相关信息成为一个复杂问题，这阻碍了充分的感知和路径规划。先前的研究在从全尺寸图像中提取全局特征方面展现了良好的性能，但它们通常难以捕捉关键的局部信息。本文提出了Crop-LSTM方法，该方法迭代地获取当前机器人位置周围的裁剪图像补丁，并预测未来的位置、方向和颠簸程度。我们的方法通过关注2D图像平面上沿预测机器人轨迹的对应图像补丁来执行局部特征提取，从而能够更准确地预测机器人未来的轨迹。利用我们的轮式移动机器人平台Raicart，我们展示了Crop-LSTM在非铺装户外环境中进行点目标导航的有效性。该方法使机器人能够在具有挑战性的非铺装户外地形中利用RGBD图像实现安全鲁棒的导航。总结视频可在https://youtu.be/iIGNZ8ignk0 获取。