Author name cluster

Peter Henry

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ICRA Conference 2014 Conference Paper

Toward online 3-D object segmentation and mapping

Evan Herbst
Peter Henry
Dieter Fox

We build on recent fast and accurate 3-D reconstruction techniques to segment objects during scene reconstruction. We take object outline information from change detection to build 3-D models of rigid objects and represent the scene as static and dynamic components. Object models are updated online during mapping, and can integrate segmentation information from sources other than change detection.

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ICRA Conference 2011 Conference Paper

Toward object discovery and modeling via 3-D scene comparison

Evan Herbst
Peter Henry
Xiaofeng Ren
Dieter Fox

The performance of indoor robots that stay in a single environment can be enhanced by gathering detailed knowledge of objects that frequently occur in that environment. We use an inexpensive sensor providing dense color and depth, and fuse information from multiple sensing modalities to detect changes between two 3-D maps. We adapt a recent SLAM technique to align maps. A probabilistic model of sensor readings lets us reason about movement of surfaces. Our method handles arbitrary shapes and motions, and is robust to lack of texture. We demonstrate the ability to find whole objects in complex scenes by regularizing over surface patches.

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ICRA Conference 2010 Conference Paper

Learning to navigate through crowded environments

Peter Henry
Christian Vollmer
Brian Ferris
Dieter Fox

The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.

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