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Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map.

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

Exploration of unknown environments remains one of the fundamental problems of mobile robotics. It is also a prime example for a task that can benefit significantly from multi-robot teams. We present an integrated system for semi-autonomous cooperative exploration, augmented by an intuitive user interface for efficient human supervision and control. In this preliminary study we demonstrate the effectiveness of the system as a whole and the intuitive interface in particular. Congruent with previous findings, results confirm that having a human in the loop improves task performance, especially with larger numbers of robots. Specific to our interface, we find that even untrained operators can efficiently manage a decently sized team of robots.