Mapping with a ground robot in GPS denied and degraded environments

Abstract

A robot system operating in an unknown environment must be able to track its position to perform its mission. Vehicles with a consistent view of the sky, e.g., aerial or water surface platforms, can reliably make use of GPS signals to correct accumulated error from inertial measurements and feature-based mapping techniques. However, ground robots that must operate across a wide range of environments suffer from additional constraints which degrade the performance of GPS such as multipath and occlusion. In this paper, we present a methodology for incorporating GPS measurements into a feature-based mapping system for two purposes: providing georeferenced coordinates for high-level mission execution and correcting accumulated map error over long-term operation. We will present both the underlying system and experimental results from a variety of relevant environments such as military training facilities and large-scale mixed indoor and outdoor environments.

Shaping of Shared Autonomous Solutions With Minimal Interaction

“…We employ onboard custom ROS components for SLAM and optimization-based trajectory control to autonomously navigate through a complex environment as described by Gregory et al ( 2016 ). Our SLAM system, which is specifically designed for operation in GPS denied environments such as building interiors and caves, is a pose-graph framework that uses 3D laser scanners and ICP to build maps of these types of environments and estimate robot trajectory (Rogers et al, 2014 ). The environment for these experiments consists of multiple concrete buildings and a street arranged and staged as a cluttered village marketplace (Figure 10 ).…”

Towards online characterization of autonomously navigating robots in unstructured environments

“…Our technique, referred to as OmniMapper, leverages the Generalized Iterative Closet Point (ICP) algorithm [24] for dense inter-frame matching of point cloud data and loop closure constraints. GPS measurements, when available, are robustly incorporated into our solution based on the techniques described in our previous work [21]. In terms of planning and navigation, we rely on the Search-Based Planning Library (SBPL) [14].…”