Author name cluster

Collin Johnson

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4 papers

2 author rows

ICRA Conference 2023 Conference Paper

The Reflectance Field Map: Mapping Glass and Specular Surfaces in Dynamic Environments

Paul Foster
Collin Johnson
Benjamin Kuipers

We present the Reflectance Field Map, a reliable real-time method for detecting shiny surfaces, like glass, metal, and mirrors, with lidar. The Reflectance Field Map combines the theory developed for Light Field Mapping, common in computer graphics, with occupancy grid mapping. Like early methods for sonar-based robot mapping, we show how the addition of angular viewpoint information to a standard 2D grid map enables robust mapping in the presence of specular reflections. However unlike previous approaches, our method works in dynamic environments. Additionally, unlike recent approaches for lidar-based mapping of specular surfaces, our approach is sensor-agnostic and has no reliance on either intensity or multi-return measurements. We demonstrate the ability of the Reflectance Field Map to accurately map a campus environment containing numerous pedestrians and significant plate glass, both straight and curved. The algorithm runs in real-time (75+Hz) on a single core of a standard desktop processor. An open source implementation of the algorithm is available at https://github.com/collinej/reflectance_field_map.

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AAMAS Conference 2017 Conference Paper

A Tale of Two Architectures: A Dual-Citizenship Integration of Natural Language and the Cognitive Map

Tom Williams
Collin Johnson
Matthias Scheutz
Benjamin Kuipers

Vulcan and DIARC are two robot architectures with very different capabilities: Vulcan uses rich spatial representations to facilitate navigation capabilities in real-world, campus-like environments, while DIARC uses high-level cognitive representations to facilitate human-like tasking through natural language. In this work, we show how the integration of Vulcan and DIARC enables not only the capabilities of the two individual architectures, but new synergistic capabilities as well, as each architecture leverages the strengths of the other. This integration presents interesting challenges, as DIARC and Vulcan are implemented in distinct multi-agent system middlewares. Accordingly, a second major contribution of this paper is the Vulcan-ADE Development Environment (VADE): a novel multi-agent system framework comprised of both (1) software agents belonging to a single robot architecture and implemented in a single multi-agent system middleware, and (2) “Dual-Citizen” agents that belong to both robot architectures and that use elements of both multi-agent system middlewares. As one example application, we demonstrate the implementation of the new joint architecture and novel multi-agent system framework on a robotic wheelchair, and show how this integration advances the state-of-the-art for NL-enabled wheelchairs.

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IROS Conference 2012 Conference Paper

Efficient search for correct and useful topological maps

Collin Johnson
Benjamin Kuipers

We present an algorithm for probabilistic topological mapping that heuristically searches a tree of map hypotheses to provide a usable topological map hypothesis online, while still guaranteeing the correct map can always be found. Our algorithm annotates each leaf of the tree with a posterior probability. When a new place is encountered, we expand hypotheses based on their posterior probability, which means only the most probable hypotheses are expanded. By focusing on the most probable hypotheses, we dramatically reduce the number of hypotheses evaluated allowing real-time operation. Additionally, our approach never prunes consistent hypotheses from the tree, which means the correct hypothesis always exists within the tree.

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IROS Conference 2012 Conference Paper

Robot navigation with model predictive equilibrium point control

Jong Jin Park
Collin Johnson
Benjamin Kuipers

An autonomous vehicle intended to carry passengers must be able to generate trajectories on-line that are safe, smooth and comfortable. Here, we propose a strategy for robot navigation in a structured, dynamic indoor environment, where the robot reasons about the near future and makes a locally optimal decision at each time step.

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