Person:
Dellaert,
Frank
Dellaert,
Frank
Permanent Link
Associated Organization(s)
Organizational Unit
ORCID
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 8 of 8
-
ItemBio-Inspired Navigation(Georgia Institute of Technology, 2011-12) Dellaert, Frank ; Gill, Tarandeep
-
ItemGeneralized Subgraph Preconditioners for Large-Scale Bundle Adjustment(Georgia Institute of Technology, 2011-11) Jian, Yong-Dian ; Balcan, Doru C. ; Dellaert, FrankWe present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either direct or iterative methods as the linear solver, GSP combines their advantages and is significantly faster on large datasets. Similar to [11], the main idea is to identify a sub-problem (subgraph) that can be solved efficiently by sparse factorization methods and use it to build a preconditioner for the conjugate gradient method. The difference is that GSP is more general and leads to much more effective preconditioners. We design a greedy algorithm to build subgraphs which have bounded maximum clique size in the factorization phase, and also result in smaller condition numbers than standard preconditioning techniques. When applying the proposed method to the “bal” datasets [1], GSP displays promising performance.
-
ItemCollaborative Stereo(Georgia Institute of Technology, 2011-09) Achtelik, Markus W. ; Weiss, Stephan ; Chli, Margarita ; Dellaert, Frank ; Siegwart, RolandIn this paper, we propose a method to recover the relative pose of two robots in absolute scale and in realtime using one monocular camera on each robot. We achieve this by fusing measurements from the onboard inertial sensors on each platform with information obtained from feature correspondences between the two cameras using an Extended Kalman Filter (EKF). This forms a flexible stereo rig, providing the ability to treat the two robots as one single dynamic sensor, which can adapt to the environment and thus improve environmental mapping, obstacle avoidance and navigation. We demonstrate the power of this approach on both simulation and real datasets, employing two micro aerial vehicles (MAVs) to illustrate successful operation over general 3D motion.
-
ItemBundle Adjustment in Large-Scale 3D Reconstructions based on Underwater Robotic Surveys(Georgia Institute of Technology, 2011-06) Beall, Chris ; Dellaert, Frank ; Mahon, Ian ; Williams, Stefan B.In this paper we present a technique to generate highly accurate reconstructions of underwater structures by employing bundle adjustment on visual features, rather than relying on a filtering approach using navigational sensor data alone. This system improves upon previous work where an extended information filter was used to estimate the vehicle trajectory. This filtering technique, while very efficient, suffers from the shortcoming that linearization errors are irreversibly incorporated into the vehicle trajectory estimate. This drawback is overcome by applying smoothing and mapping to the full problem. In contrast to the filtering approach, smoothing and mapping techniques solve for the entire vehicle trajectory and landmark positions at once by performing bundle adjustment on all the visual measurements taken at each frame. We formulate a large nonlinear least-squares problem where we minimize the pixel projection error of each of the landmark measurements. The technique is demonstrated on a large-scale underwater dataset, and it is also shown that superior results are achieved with smoothing and mapping as compared to the filtering approach.
-
ItemCAREER: Markov Chain Monte Carlo methods for large scale correspondence problems in computer vision and robotics(Georgia Institute of Technology, 2011-05-17) Dellaert, Frank ; Khan, Zia ; Potthast, Christian
-
ItemOnline Probabilistic Topological Mapping(Georgia Institute of Technology, 2011-01-24) Ranganathan, Ananth ; Dellaert, FrankWe present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao–Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.
-
ItemiSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering(Georgia Institute of Technology, 2011) Kaess, Michael ; Johannsson, Hordur ; Roberts, Richard ; Ila, Viorela ; Leonard, John ; Dellaert, FrankWe present iSAM2, a fully incremental, graphbased version of incremental smoothing and mapping (iSAM). iSAM2 is based on a novel graphical model-based interpretation of incremental sparse matrix factorization methods, afforded by the recently introduced Bayes tree data structure. The original iSAM algorithm incrementally maintains the square root information matrix by applying matrix factorization updates. We analyze the matrix updates as simple editing operations on the Bayes tree and the conditional densities represented by its cliques. Based on that insight, we present a new method to incrementally change the variable ordering which has a large effect on efficiency. The efficiency and accuracy of the new method is based on fluid relinearization, the concept of selectively relinearizing variables as needed. This allows us to obtain a fully incremental algorithm without any need for periodic batch steps. We analyze the properties of the resulting algorithm in detail, and show on various real and simulated datasets that the iSAM2 algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
-
ItemVisibility Learning in Large-Scale Urban Environment(Georgia Institute of Technology, 2011) Alcantarilla, Pablo F. ; Ni, Kai ; Bergasa, Luis M. ; Dellaert, Frank