Whereas dedicated scene representations are required for each different tasks in conventional robotic systems, this paper demonstrates that a unified representation can be used directly for multiple key tasks. We propose the Log-Gaussian Process Implicit Surface for Mapping, Odometry and Planning (Log-GPIS-MOP): a probabilistic framework for surface reconstruction, localisation and navigation based on a unified representation. Our framework applies a logarithmic transformation to a Gaussian Process Implicit Surface (GPIS) formulation to recover a global representation that accurately captures the Euclidean distance field with gradients and, at the same time, the implicit surface. By directly estimate the distance field and its gradient through Log-GPIS inference, the proposed incremental odometry technique computes the optimal alignment of an incoming frame, and fuses it globally to produce a map. Concurrently, an optimisation-based planner computes a safe collision-free path using the same Log-GPIS surface representation. We validate the proposed framework on simulated and real datasets in 2D and 3D and benchmark against the state-of-the-art approaches. Our experiments show that Log-GPIS-MOP produces competitive results in sequential odometry, surface mapping and obstacle avoidance.
翻译:虽然常规机器人系统中的每项不同任务都需要专门的场面说明,但本文件表明,可以直接使用统一的场面说明,用于多项关键任务。我们提议用Log-GPIS(Log-GPIS-MOP)来直接估计测地、测地和规划的Law-Gaussian进程隐地表层(Log-GPIS-MOP):一个基于统一代表的地表重建、定位和导航的概率框架。我们的框架对高萨进程隐地表层(GPIS)的配方进行对数转换,以恢复一个全球代表,以精确地用梯度和隐含表面来捕捉欧几里德距离场。我们通过Log-GIS(Log-GIS)的推断直接估计距离场及其梯度。提议的递增量测量技术通过直接估计测地表场及其梯度,计算进地表框架的最佳匹配框架,并结合全球绘制地图。同时,基于选地平图的仪表实验用相同的测地表结果,我们用2D和3D的模拟和测地平地表试验结果。