Sunday, April 25, 2010

Lab Meeting April 27th (Andi): Error Modeling and Calibration of Exteroceptive Sensors for Accurate Mapping Applications

Authors: James P. Underwood, Andrew Hill, Thierry Peynot, and Steven J. Scheding
ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics

Abstract: Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that
captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should
be employed whenever maps are formed from range-based exteroceptive sensor data.

full paper

Sunday, April 18, 2010

Lab Meeting April 20th, 2010 (KuoHuei): Directing Crowd Simulations Using Navigation Fields (TVCG 2010)

Title: Directing Crowd Simulations Using Navigation Fields
(to appear in IEEE Transactions on Visualization and Computer Graphics)

Authors: Sachin Patil,Jur van den Berg,Sean Curtis,Ming Lin,Dinesh Manocha

Abstract: We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents towards desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with most current local collision-avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.

Link: [web] [pdf]

Lab Meeting April 20th, 2010 (Jeff): iSAM: Incremental Smoothing and Mapping

Title: iSAM: Incremental Smoothing and Mapping

Authors: Michael Kaess, Ananth Ranganathan, and Frank Dellaert


In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries
that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld
datasets for both landmark and pose-only settings.

IEEE Transactions on Robotics, Vol. 24, No. 6, December 2008:1365-1378

Thursday, April 08, 2010

News: Grand Challenges of Science: Robotics

In January, DISCOVER and the National Science Foundation continued their Grand Challenges event series with a panel discussion at Carnegie Mellon University exploring the dynamic world of robotics.

The panel included four eminent roboticists—Javier Movellan from University of California San Diego, Rodney Brooks from the Massachussetts Institute of Technology, William "Red" Whittaker from Carnegie Mellon University, and Robyn Murphy from Texas A&M University—who discussed some the big questions on the future of their field: How will robots transform industry, health care, and warfare? Will they ever be our equals? The conversation was moderated by DISCOVER editor-in-chief Corey Powell.

See the video

Wednesday, April 07, 2010

Lab Meeting April 13, 2010 (Alan) - Information-Based Compact Pose SLAM (T-RO 2010)

Title: Information-Based Compact Pose SLAM
Authors: Viorela Ila, Josep M. Porta, and Juan Andrade-Cetto, Member, IEEE


Abstract—Pose SLAMis the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of PoseSLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.

Sunday, April 04, 2010

Researchers develop a robot that folds towels

More than a household convenience, the project is a breakthrough in the robotic manipulation of non-rigid objects

Check out their ICRA 2010 paper.