While the study of geometry has mainly concentrated on single viewpoint (SVP) cameras, there is growing attention to more general non-SVP systems. Here, we study an important class of systems that inherently have a non-SVP: a perspective camera imaging through an interface into a medium. Such systems are ubiquitous: They are common when looking into water-based environments. The paper analyzes the common flat-interface class of systems. It characterizes the locus of the viewpoints (caustic) of this class and proves that the SVP model is invalid in it. This may explain geometrical errors encountered in prior studies. Our physics-based model is parameterized by the distance of the lens from the medium interface, besides the focal length. The physical parameters are calibrated by a simple approach that can be based on a single frame. This directly determines the system geometry. The calibration is then used to compensate for modeled system distortion. Based on this model, geometrical measurements of objects are significantly more accurate than if based on an SVP model. This is demonstrated in real-world experiments. In addition, we examine by simulation the errors expected by using the SVP model. We show that when working at a constant range, the SVP model can be a good approximation.

Abstract — The Arctic seafloor remains one of the last unexplored areas on Earth. Exploration of this unique environment using standard remotely operated oceanographic tools has been obstructed by the dense Arctic ice cover. In the summer of 2007 the Arctic Gakkel Vents Expedition (AGAVE) was conducted with the express intention of understanding aspects of the marine biology, chemistry and geology associated with hydrothermal venting on the section of the mid-ocean ridge known as the Gakkel Ridge. Unlike previous research expeditions to the Arctic the focus was on high resolution imaging and sampling of the deep seafloor. To accomplish our goals we designed two new Autonomous Underwater Vehicles (AUVs) named Jaguar and Puma, which performed a total of nine dives at depths of up to 4062m. These AUVs were used in combination with a towed vehicle and a conventional CTD (conductivity, temperature and depth) program to characterize the seafloor. This paper describes the design decisions and operational changes required to ensure useful service, and facilitate deployment, operation, and recovery in the unique Arctic environment. I.

In order for humans and robots to work effectively together, they need to be able to converse about abilities, goals and achievements. Thus, we are developing an interaction infrastructure called the "Human-Robot Interaction Operating System" (HRI/OS). The HRI/OS provides a structured software framework for building human-robot teams, supports a variety of user interfaces, enables humans and robots to engage in task-oriented dialogue, and facilitates integration of robots through an extensible API.

The Peer-To-Peer Human-Robot Interaction (P2P-HRI) project is developing techniques to improve task coordination and collaboration between human and robot partners. Our work is motivated by the need to develop effective human-robot teams for space mission operations. A central element of our approach is creating dialogue and interaction tools that enable humans and robots to flexibly support one another. In order to understand how this approach can influence task performance, we recently conducted a series of tests simulating a lunar construction task with a human-robot team. In this paper, we describe the tests performed, discuss our initial results, and analyze the effect of intervention on task performance.

Modern NASA planetary exploration missions employ complex systems of hardware and software managed by large teams of engineers and scientists in order to study remote environments. The most complex and successful of these recent projects is the Mars Exploration Rover mission. The Computational Sciences Division at NASA Ames Research Center delivered a 3D visualization program, Viz, to the MER mission that provides an immersive, interactive environment for science analysis of the remote planetary surface. In addition, Ames provided the Athena Science Team with high-quality terrain reconstructions generated with the Ames Stereo-pipeline. The on-site support team for these software systems responded to unanticipated opportunities to generate 3D terrain models during the primary MER mission. This paper describes Viz, the Stereo-pipeline, and the experiences of the on-site team supporting the scientists at JPL during the primary MER mission.

Future Mars rovers, such as the planned 2009 MSL rover, require sufficient autonomy to robustly approach rock targets and place an instrument in contact with them. It took the 1997 Sojourner Mars rover between 3 and 5 communications cycles to accomplish this. This paper describes the NASA Ames approach to robustly accomplishing single cycle instrument deployment, using the K9 prototype Mars rover. An off-board 3D site model is used to select science targets for the rover. K9 navigates to targets using deduced reckoning, and autonomously assesses the target area to determine where to place an arm mounted microscopic camera. Onboard K9 is a resource cognizant conditional executive, which extends the complexity and duration of operations that a can be accomplished without intervention from mission control.

This paper presents an efficient and robust method for registration of terrain models created using stereovision on a planetary rover. Our approach projects two surface models into a virtual depth map, rendering the models, as they would be seen from a single range sensor. Correspondence is established based on which points project to the same location in the virtual range sensor. A robust norm of the deviations in observed depth is used as the objective function, and the algorithm searches for the rigid transformation, which minimizes the norm. An initial coarse search is done using rover pose information from odometry and orientation sensing. A fine search is done using Levenberg-Marquardt. Our method enables a planetary rover to keep track of designated science targets as it moves, and to hand off targets from one set of stereo cameras to another. These capabilities are essential for the rover to autonomously approach a science target and place an instrument in contact in a single command cycle.

This paper summarizes a long-term project to install and maintain socially interactive, autonomous mobile robots in public spaces. We have deployed four robots over the past five years, accumulating a total operational time exceeding seven years. This document introduces the robots, then focuses on lessons learned from each deployment. Finally, this paper describes how this entire project came to a close, offering a cautionary tale for those who wish to embark on such an effort in the future.

We present a robot, called InductoBeast, that greets a new office building by learning the floorplan automatically, with minimal human intervention and a priori knowledge. Our robot architecture is unique because it combines both abductive and inductive mapping methods to solve this problem. We present experimental results spanning three office environments. We hope the breadth of these results helps to establish a performance benchmark against which robust and adaptive navigator robots of the future may be measured.