Person:

Dellaert, Frank

Permanent Link

https://hdl.handle.net/1853/71202

Associated Organization(s)

Organizational Unit

School of Interactive Computing

School established in 2007

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Publication Search Results

Now showing 1 - 10 of 56

Information-based Reduced Landmark SLAM

(Georgia Institute of Technology, 2015-05) Choudhary, Siddharth ; Indelman, Vadim ; Christensen, Henrik I. ; Dellaert, Frank

In 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.
Support-Theoretic Subgraph Preconditioners for Large-Scale SLAM

(Georgia Institute of Technology, 2013-11) Jian, Yong-Dian ; Balcan, Doru ; Panageas, Ioannis ; Tetali, Prasad ; Dellaert, Frank

Efficiently 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.
Incremental Light Bundle Adjustment for Robotics Navigation

(Georgia Institute of Technology, 2013-11) Indelman, Vadim ; Melim, Andrew ; Dellaert, Frank

This 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.
Path Planning with Uncertainty: Voronoi Uncertainty Fields

(Georgia Institute of Technology, 2013-05) Ok, Kyel ; Ansari, Sameer ; Gallagher, Billy ; Sica, William ; Dellaert, Frank ; Stilman, Mike

In 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.
DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping

(Georgia Institute of Technology, 2013-05) Cunningham, Alexander ; Indelman, Vadim ; Dellaert, Frank

This 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.
iSAM2: 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, Frank

We 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.
Visibility Learning in Large-Scale Urban Environment

(Georgia Institute of Technology, 2011) Alcantarilla, Pablo F. ; Ni, Kai ; Bergasa, Luis M. ; Dellaert, Frank
Effects of sensory precision on mobile robot localization and mapping

(Georgia Institute of Technology, 2010-12) Rogers, John G. ; Trevor, Alexander J. B. ; Nieto-Granda, Carlos ; Cunningham, Alexander ; Paluri, Manohar ; Michael, Nathan ; Dellaert, Frank ; Christensen, Henrik I. ; Kumar, Vijay

This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.
Multi-Level Submap Based SLAM Using Nested Dissection

(Georgia Institute of Technology, 2010) Ni, Kai ; Dellaert, Frank

We propose a novel batch algorithm for SLAM problems that distributes the workload in a hierarchical way. We show that the original SLAM graph can be recursively partitioned into multiple-level submaps using the nested dissection algorithm, which leads to the cluster tree, a powerful graph representation. By employing the nested dissection algorithm, our algorithm greatly minimizes the dependencies between two subtrees, and the optimization of the original SLAM graph can be done using a bottom-up inference along the corresponding cluster tree. To speed up the computation, we also introduce a base node for each submap and use it to represent the rigid transformation of the submap in the global coordinate frame. As a result, the optimization moves the base nodes rather than the actual submap variables. We demonstrate that our algorithm is not only exact but also much faster than alternative approaches in both simulations and real-world experiments.
Subgraph-preconditioned Conjugate Gradients for Large Scale SLAM

(Georgia Institute of Technology, 2010) Dellaert, Frank ; Carlson, Justin ; Ila, Viorela ; Ni, Kai ; Thorpe, Charles E.

In this paper we propose an efficient preconditioned conjugate gradients (PCG) approach to solving large-scale SLAM problems. While direct methods, popular in the literature, exhibit quadratic convergence and can be quite efficient for sparse problems, they typically require a lot of storage as well as efficient elimination orderings to be found. In contrast, iterative optimization methods only require access to the gradient and have a small memory footprint, but can suffer from poor convergence. Our new method, subgraph preconditioning, is obtained by re-interpreting the method of conjugate gradients in terms of the graphical model representation of the SLAM problem. The main idea is to combine the advantages of direct and iterative methods, by identifying a sub-problem that can be easily solved using direct methods, and solving for the remaining part using PCG. The easy sub-problems correspond to a spanning tree, a planar subgraph, or any other substructure that can be efficiently solved. As such, our approach provides new insights into the performance of state of the art iterative SLAM methods based on re-parameterized stochastic gradient descent. The efficiency of our new algorithm is illustrated on large datasets, both simulated and real.