Showing papers by "Larry Matthies published in 2013"

First-Person Activity Recognition: What Are They Doing to Me?

Abstract: This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.

High fidelity day/night stereo mapping with vegetation and negative obstacle detection for vision-in-the-loop walking

Abstract: This paper describes the stereo vision near-field terrain mapping system used by the Legged Squad Support System (LS3) quadruped vehicle to automatically adjust its gait in complex natural terrain. The mapping system achieves high robustness with a combination of stereo model-based outlier rejection and spatial and temporal filtering, enabled by a unique hybrid 2D/3D data structure. Classification of sparse structures allows the vehicle to traverse through vegetation. Inference of negative obstacles allows the vehicle to avoid steep drop-offs. A custom designed near-infrared illumination system enables operation at night. The mapping system has been tested extensively with controlled experiments and 72km of field testing in a wide variety of terrains and conditions.

4DoF drift free navigation using inertial cues and optical flow

Abstract: In this paper, we describe a novel approach in fusing optical flow with inertial cues (3D acceleration and 3D angular velocities) in order to navigate a Micro Aerial Vehicle (MAV) drift free in 4DoF and metric velocity. Our approach only requires two consecutive images with a minimum of three feature matches. It does not require any (point) map nor any type of feature history. Thus it is an inherently failsafe approach that is immune to map and feature-track failures. With these minimal requirements we show in real experiments that the system is able to navigate drift free in all angles including yaw, in one metric position axis, and in 3D metric velocity. Furthermore, it is a power-on-and-go system able to online self-calibrate the inertial biases, the visual scale and the full 6DoF extrinsic transformation parameters between camera and IMU.

Model-based autonomous system for performing dexterous, human-level manipulation tasks

Abstract: United States. Defense Advanced Research Projects Agency. Autonomous Robotic Manipulation Program