论文标题
A1猛击：使用A1的板载传感器四倍的大满贯
A1 SLAM: Quadruped SLAM using the A1's Onboard Sensors
论文作者
论文摘要
四足动物是在过去几年中引起的机器人，因为它们在多种应用中在各种地形和实用程序中导航的多功能性。为了使四足动物在没有预定义的地图的情况下导航，他们必须依靠SLAM方法来本地化和构建环境地图。尽管SLAM和四足动物的兴趣和研究开发激增，但仍未有一个开源软件包，该软件包大写了负担得起的四足动物的机载传感器。这激发了A1 SLAM软件包，该软件包是一个开源式ROS软件包，使用使用机器人运送的默认传感器为实时，高性能的SLAM功能提供了Unitree A1四倍。 A1 SLAM使用因子图范式解决了Poseslai问题，以优化整个轨迹的姿势。该算法的主要设计特征是使用完全连接的激光镜探测因子的滑动窗口。 A1 SLAM已针对Google的制图师进行了基准测试，并且表现出卓越的性能，尤其是在经历了积极运动的轨迹方面。
Quadrupeds are robots that have been of interest in the past few years due to their versatility in navigating across various terrain and utility in several applications. For quadrupeds to navigate without a predefined map a priori, they must rely on SLAM approaches to localize and build the map of the environment. Despite the surge of interest and research development in SLAM and quadrupeds, there still has yet to be an open-source package that capitalizes on the onboard sensors of an affordable quadruped. This motivates the A1 SLAM package, which is an open-source ROS package that provides the Unitree A1 quadruped with real-time, high performing SLAM capabilities using the default sensors shipped with the robot. A1 SLAM solves the PoseSLAM problem using the factor graph paradigm to optimize for the poses throughout the trajectory. A major design feature of the algorithm is using a sliding window of fully connected LiDAR odometry factors. A1 SLAM has been benchmarked against Google's Cartographer and has showed superior performance especially with trajectories experiencing aggressive motion.
