We present a novel method for fully automated exterior calibration of a 2D scanning laser range sensor that attains accurate pose with respect to a fixed 3D reference frame. This task is crucial for applications that attempt to recover self-consistent 3D environment maps and produce accurately registered or fused sensor data.A key contribution of our approach lies in the design of a class of calibration target objects whose pose can be reliably recognized from a single observation (i.e. from one 2D range data stripe). Unlike other techniques, we do not require simultaneous camera views or motion of the sensor, making our approach simple, flexible and environment-independent.In this paper we illustrate the target geometry and derive the relationship between a single 2D range scan and the 3D sensor pose. We describe an algorithm for closed-form solution of the 6 DOF pose that minimizes an algebraic error metric, and an iterative refinement scheme that subsequently minimizes geometric error. Finally, we report performance and stability of our technique on synthetic and real data sets, and demonstrate accuracy within 1 degree of orientation and 3 cm of position in a realistic configuration.

One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in human workplaces, and be accepted by the human occupants. We describe the development of a multi-ton robotic forklift intended to operate alongside people and vehicles, handling palletized materials within existing, active outdoor storage facilities.The system has four novel characteristics. The first is a multimodal interface that allows users to efficiently convey task-level commands to the robot using a combination of pen-based gestures and natural language speech. These tasks include the manipulation, transport, and placement of palletized cargo within dynamic, human-occupied warehouses. The second is the robot's ability to learn the visual identity of an object from a single user-provided example and use the learned model to reliably and persistently detect objects despite significant spatial and temporal excursions. The third is a reliance on local sensing that allows the robot to handle variable palletized cargo and navigate within dynamic, minimally-prepared environments without GPS. The fourth concerns the robot's operation in close proximity to people, including its human supervisor, pedestrians who may cross or block its path, moving vehicles, and forklift operators who may climb inside the robot and operate it manually. This is made possible by interaction mechanisms that facilitate safe, effective operation around people.This paper provides a comprehensive description of the system's architecture and implementation, indicating how real-world operational requirements motivated key design choices. We offer qualitative and quantitative analyses of the robot operating in real settings and discuss the lessons learned from our effort.Put the pallet of pipes on the truck Figure 1: (left) The mobile manipulation platform is designed to safely coexist with people within unstructured environments while performing material handling under the direction of humans.

We describe a vision-based algorithm that enables a robot to robustly detect specific objects in a scene following an initial segmentation hint from a human user. The novelty lies in the ability to 'reacquire' objects over extended spatial and temporal excursions within challenging environments based upon a single training example. The primary difficulty lies in achieving an effective reacquisition capability that is robust to the effects of local clutter, lighting variation, and object relocation. We overcome these challenges through an adaptive detection algorithm that automatically generates multipleview appearance models for each object online. As the robot navigates within the environment and the object is detected from different viewpoints, the one-shot learner opportunistically and automatically incorporates additional observations into each model. In order to overcome the effects of 'drift' common to adaptive learners, the algorithm imposes simple requirements on the geometric consistency of candidate observations. Motivating our reacquisition strategy is our work developing a mobile manipulator that interprets and autonomously performs commands conveyed by a human user. The ability to detect specific objects and reconstitute the user's segmentation hints enables the robot to be situationally aware. This situational awareness enables rich command and control mechanisms and affords natural interaction. We demonstrate one such capability that allows the human to give the robot a 'guided tour' of named objects within an outdoor environment and, hours later, to direct the robot to manipulate those objects by name using spoken instructions. We implemented our appearance-based detection strategy on our robotic manipulator as it operated over multiple days in different outdoor environments. We evaluate the algorithm's performance under challenging conditions that include scene clutter, lighting and viewpoint variation, object ambiguity, and object relocation. The results demonstrate a reacquisition capability that is effective in real-world settings.

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This paper describes an algorithm enabling a human supervisor to convey task-level information to a robot by using stylus gestures to circle one or more objects within the field of view of a robot-mounted camera. These gestures serve to segment the unknown objects from the environment. Our method's main novelty lies in its use of appearancebased object "reacquisition" to reconstitute the supervisory gestures (and corresponding segmentation hints), even for robot viewpoints spatially and/or temporally distant from the viewpoint underlying the original gesture. Reacquisition is particularly challenging within relatively dynamic and unstructured environments. The technical challenge is to realize a reacquisition capability robust enough to appearance variation to be useful in practice. Whenever the supervisor indicates an object, our system builds a feature-based appearance model of the object. When the object is detected from subsequent viewpoints, the system automatically and opportunistically incorporates additional observations, revising the appearance model and reconstituting the rough contours of the original circling gesture around that object. Our aim is to exploit reacquisition in order to both decrease the user burden of task specification and increase the effective autonomy of the robot. We demonstrate and analyze the approach on a robotic forklift designed to approach, manipulate, transport and place palletized cargo within an outdoor warehouse. We show that the method enables gesture reuse over long timescales and robot excursions (tens of minutes and hundreds of meters).