从裁剪图像块中学习车辆动力学，用于非铺装户外地形中的机器人导航
Learning Vehicle Dynamics from Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains
AI总结：
针对非铺装户外地形中机器人导航的挑战，提出Crop-LSTM方法，通过迭代裁剪图像块并沿预测轨迹提取局部特征，准确预测未来位置、朝向和颠簸，实现安全稳健的点目标导航。
摘要：
在自主移动机器人领域，安全穿越非铺装户外环境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。先前的工作在全尺寸图像上提取全局特征方面显示出有前景的性能。然而，它们通常在捕捉关键局部信息方面面临挑战。在本文中，我们提出了Crop-LSTM，它迭代地获取当前机器人位置周围的裁剪图像块，并预测未来位置、朝向和颠簸程度。我们的方法通过在2D图像平面中沿预测的机器人轨迹关注相应的图像块来执行局部特征提取。这使得对机器人未来轨迹的预测更加准确。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。我们的方法在具有挑战性的非铺装户外地形中，使用RGBD图像实现了安全且稳健的导航。总结视频可在https://youtu.be/iIGNZ8ignk0获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
