Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Decomposable Bundle Adjustment Using a Junction TreeThe Sparse Bundle Adjustment (SBA) algorithm is a widely used method to solve multi-view reconstruction problems in vision. The critical cost of SBA depends on the fill in of the reduced camera matrix whose pattern is known as the Secondary structure of the problem. In centered object applications where a large number of images are taken in a small area the camera matrix obtained when points are eliminated is dense. On the contrary, visual mapping systems where long trajectories are traversed yield sparse matrices. In this paper, we propose a Decomposable Bundle Adjustment (DBA) method which naturally adapts to the fill in pattern of the camera matrix improving the performance on visual mapping systems. The proposed algorithm is able to decompose the normal equations into small subsystems which are ordered in a junction tree structure. To solve the original system, local factorizations of the small dense matrices are passed between clusters in the tree. The DBA algorithm has been tested for simulated and real data experiments for different environment configurations showing good performance.
An Incremental Trust-Region Method for Robust Online Sparse Least-Squares EstimationMany online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals. Under these conditions, maximum likelihood estimation corresponds to solving a sequence of sparse least-squares minimization problems in which additional summands are added to the objective function over time. In this paper we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg trust-region method suitable for use in online sparse least-squares minimization. As a trust-region method, Powell's Dog-Leg enjoys excellent global convergence properties, and is known to be considerably faster than both Gauss-Newton and Levenberg-Marquardt when applied to sparse least-squares problems. Consequently, RISE maintains the speed of current state-of-the-art incremental sparse least-squares methods while providing superior robustness to objective function nonlinearities.
Weak Constraints Network OptimiserWe present a general framework to estimate the parameters of both a robot and landmarks in 3D. It relies on the use of a stochastic gradient descent method for the optimisation of the nodes in a graph of weak constraints where the landmarks and robot poses are the nodes. Then a belief propagation method combined with covariance intersection is used to estimate the uncertainties of the nodes. The first part of the article describes what is needed to define a constraint and a node models, how those models are used to update the parameters and the uncertainties of the nodes. The second part present the models used for robot poses and interest points, as well as simulation results.
Multi-Agent Deterministic Graph Mapping Via Robot RendezvousIn this paper, we present a novel algorithm for deterministically mapping an undirected graph-like world with multiple synchronized agents. The application of this algorithm is the collective mapping of an indoor environment with multiple mobile robots while leveraging an embedded topological decomposition of the environment. Our algorithm relies on a group of agents that all depart from the same initial vertex in the graph and spread out to explore the graph. A centralized tree of graph hypotheses is maintained to consider loop-closure, which is deterministically verified when agents observe each other at a common vertex. To achieve efficient mapping, we introduce an active exploration method in which agents dynamically request rendezvous tasks from other available agents to validate graph hypotheses.