森林环境深度数据集

森林环境深度数据集是由南安普顿大学的电子与计算机科学学院创建，旨在为森林环境中的自主机器人提供深度预测支持。数据集包含超过134000对RGB和深度图像，其中精选的9700对图像具有同步的IMU、旋转编码器和GPS信息。数据集通过定制的采集装置在森林中不同时间和天气条件下收集，涵盖了多种自然场景元素。创建过程中，数据经过校准、同步、对齐和时间戳处理，确保数据质量。该数据集主要应用于森林机器人的路径规划和地形可通行性分析，解决森林环境中机器人自主导航的挑战。

The Forest Environment Depth Dataset was developed by the School of Electronics and Computer Science at the University of Southampton, with the aim of providing depth prediction support for autonomous robots operating in forest environments. The dataset contains over 134,000 pairs of RGB and depth images, among which 9,700 carefully selected pairs come with synchronized IMU, rotary encoder and GPS data. Collected using a custom data acquisition setup in forests across different time periods and weather conditions, the dataset covers a wide range of natural scene elements. During the dataset construction process, all data underwent calibration, synchronization, alignment and timestamp processing to ensure data quality. This dataset is primarily applied to path planning and terrain traversability analysis for forest robots, addressing the challenges of autonomous robot navigation in forest environments.