Person:
Dellaert,
Frank
Dellaert,
Frank
Permanent Link
Associated Organization(s)
Organizational Unit
ORCID
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 7 of 7
-
ItemInformation-based Reduced Landmark SLAM(Georgia Institute of Technology, 2015-05) Choudhary, Siddharth ; Indelman, Vadim ; Christensen, Henrik I. ; Dellaert, FrankIn this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate without compromising the accuracy of the estimated trajectory. We also propose an incremental version of the reduction algorithm which can be used in SLAM framework resulting in information based reduced landmark SLAM. The results of reduced landmark based SLAM algorithm are shown on Victoria park dataset and a Synthetic dataset and are compared with standard graph SLAM (SAM [6]) algorithm. We demonstrate a reduction of 40-50% in the number of landmarks and around 55% in the number of poses with minimal estimation error as compared to standard SLAM algorithm.
-
ItemIncremental Light Bundle Adjustment for Robotics Navigation(Georgia Institute of Technology, 2013-11) Indelman, Vadim ; Melim, Andrew ; Dellaert, FrankThis paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points. Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion [10], to information fusion in robotics navigation and in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain high-rate performance over time, consecutive IMU measurements are summarized using a recently-developed technique and navigation states are added to the optimization only at camera rate. If required, the observed 3D points can be reconstructed at any time based on the optimized robot’s poses. The proposed method is compared to BA both in terms of accuracy and computational complexity in a statistical simulation study.
-
ItemSupport-Theoretic Subgraph Preconditioners for Large-Scale SLAM(Georgia Institute of Technology, 2013-11) Jian, Yong-Dian ; Balcan, Doru ; Panageas, Ioannis ; Tetali, Prasad ; Dellaert, FrankEfficiently solving large-scale sparse linear systems is important for robot mapping and navigation. Recently, the subgraph-preconditioned conjugate gradient method has been proposed to combine the advantages of two reigning paradigms, direct and iterative methods, to improve the efficiency of the solver. Yet the question of how to pick a good subgraph is still open. In this paper, we propose a new metric to measure the quality of a spanning tree preconditioner based on support theory. We use this metric to develop an algorithm to find good subgraph preconditioners and apply them to solve the SLAM problem. The results show that although the proposed algorithm is not fast enough, the new metric is effective and resulting subgraph preconditioners significantly improve the efficiency of the state-of-the-art solver.
-
ItemDDF-SAM 2.0: Consistent Distributed Smoothing and Mapping(Georgia Institute of Technology, 2013-05) Cunningham, Alexander ; Indelman, Vadim ; Dellaert, FrankThis paper presents an consistent decentralized data fusion approach for robust multi-robot SLAM in dan- gerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neigh- borhood information in a single, consistent augmented local map, without the overly conservative approach to avoiding information double-counting in the previous DDF-SAM algo- rithm. We introduce the anti-factor as a means to subtract information in graphical SLAM systems, and illustrate its use to both replace information in an incremental solver and to cancel out neighborhood information from shared summarized maps. This paper presents and compares three summarization techniques, with two exact approaches and an approximation. We evaluated the proposed system in a synthetic example and show the augmented local system and the associated summarization technique do not double-count information, while keeping performance tractable.
-
ItemPath Planning with Uncertainty: Voronoi Uncertainty Fields(Georgia Institute of Technology, 2013-05) Ok, Kyel ; Ansari, Sameer ; Gallagher, Billy ; Sica, William ; Dellaert, Frank ; Stilman, MikeIn this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modified generalized Voronoi diagrams to guarantee finding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertainty- biased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.
-
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