  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.
