RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps.We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.

Computer Science 2

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

We survey termination analysis techniques for Logic Programs. We give an extensive introduction to the topic. We recall several motivations for the work, and point out the intuitions behind a number of LP-specific issues that turn up, such as: the study of different classes of programs and LP languages, of different classes of queries and of different selection rules, the difference between existential and universal termination, and the treatment of backward unification and local variables. Then, we turn to more technical aspects: the structure of the termination proofs, the selection of well-founded orderings, norms and level mappings, the inference of interargument relations, and special treatments proposed for dealing with mutual recursion. For each of these, we briefly sketch the main approaches presented in the literature, using a fixed example as a file rouge. We conclude with some comments on loop detection and cycle unification and state some open problems.

Termination of logic programs: the never-ending story

We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).

Towards Reliable Grasping and Manipulation in Household Environments

Light Detection and Ranging (LIDAR) sensors have become increasingly common in both industrial and robotic applications. LIDAR sensors are particularly desirable for their direct distance measurements and high accuracy, but traditionally have been configured with only a single rotating beam. However, recent technological progress has spawned a new generation of LIDAR sensors equipped with many simultaneous rotating beams at varying angles, providing at least an order of magnitude more data than single-beam LIDARs and enabling new applications in mapping [6], object detection and recognition [15], scene understanding [16], and SLAM [9].

/pdf/unsupervised-calibration-for-multi-beam-lasers-2edl5x6szx.pdf

Unsupervised Calibration for Multi-beam Lasers

Complex robots with multiple arms and sensors need good calibration to perform precise tasks in unstructured environments. The sensors must be calibrated both to the manipulators and to each other, since fused sensor data is often needed. We propose an extendable framework that combines measurements from the robot’s various sensors (proprioceptive and external) to calibrate the robot’s joint offsets and external sensor locations. Our approach is unique in that it accounts for sensor measurement uncertainties, thereby allowing sensors with very different error characteristics to be used side by side in the calibration. The framework is general enough to handle complex robots with kinematic components, including external sensors on kinematic chains. We validate the framework by implementing it on the Willow Garage PR2 robot, providing a significant improvement in the robot’s calibration.

Computer Science 2

Citations

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

Termination of logic programs: the never-ending story

Towards Reliable Grasping and Manipulation in Household Environments

Unsupervised Calibration for Multi-beam Lasers

Calibrating a Multi-arm Multi-sensor Robot: A Bundle Adjustment Approach

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