Log-GPIS-MOP: A Unified Representation for Mapping, Odometry, and Planning
Lan Wu, Ki Myung Brian Lee, Cédric Le Gentil, Teresa Vidal‐Calleja
Abstract
Whereas dedicated scene representations are required for each different task in conventional robotic systems, this article 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, localization, 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 estimating 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 optimization-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 2-D and 3-D, 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.