In this paper, we propose the FoMo (For\^et Montmorency) dataset: a comprehensive, multi-season data collection. Located in the Montmorency Forest, Quebec, Canada, our dataset will capture a rich variety of sensory data over six distinct trajectories totaling 6 kilometers, repeated through different seasons to accumulate 42 kilometers of recorded data. The boreal forest environment increases the diversity of datasets for mobile robot navigation. This proposed dataset will feature a broad array of sensor modalities, including lidar, radar, and a navigation-grade Inertial Measurement Unit (IMU), against the backdrop of challenging boreal forest conditions. Notably, the FoMo dataset will be distinguished by its inclusion of seasonal variations, such as changes in tree canopy and snow depth up to 2 meters, presenting new challenges for robot navigation algorithms. Alongside, we will offer a centimeter-level accurate ground truth, obtained through Post Processed Kinematic (PPK) Global Navigation Satellite System (GNSS) correction, facilitating precise evaluation of odometry and localization algorithms. This work aims to spur advancements in autonomous navigation, enabling the development of robust algorithms capable of handling the dynamic, unstructured environments characteristic of boreal forests. With a public odometry and localization leaderboard and a dedicated software suite, we invite the robotics community to engage with the FoMo dataset by exploring new frontiers in robot navigation under extreme environmental variations. We seek feedback from the community based on this proposal to make the dataset as useful as possible. For further details and supplementary materials, please visit https://norlab-ulaval.github.io/FoMo-website/.

翻译：本文提出FoMo（Forêt Montmorency）数据集：一个全面的多季节数据采集方案。该数据集位于加拿大魁北克省蒙特莫朗西森林，沿六条独立轨迹（总长6公里）在不同季节重复采集，累计42公里记录数据。北方森林环境丰富了移动机器人导航数据集的多样性。本数据集将包含涵盖激光雷达、雷达及导航级惯性测量单元（IMU）等广泛传感器模态，并置身于充满挑战的北方森林环境背景下。值得关注的是，FoMo数据集将因包含季节变化特征（如树冠变化和深达2米的积雪）而独具特色，为机器人导航算法带来全新挑战。同时，我们将通过后处理动态（PPK）全球导航卫星系统（GNSS）校正技术提供厘米级精度真值数据，以促进里程计与定位算法的精确评估。本研究旨在推动自主导航技术发展，助力开发能应对北方森林动态非结构化环境的鲁棒算法。通过公开的里程计与定位排行榜及专用软件套件，我们诚邀机器人社区探索极端环境变化下的导航新前沿。欢迎社区基于本提案提供反馈，以最大程度优化数据集实用性。更多详情及补充材料请见 https://norlab-ulaval.github.io/FoMo-website/。