3 research outputs found
Incremental RANSAC for Online Relocation in Large Dynamic Environments
Vehicle relocation is the problem in which a mobile robot has to estimate the
self-position with respect to an a priori map of landmarks using the perception
and the motion measurements without using any knowledge of the initial
self-position. Recently, RANdom SAmple Consensus (RANSAC), a robust
multi-hypothesis estimator, has been successfully applied to offline relocation
in static environments. On the other hand, online relocation in dynamic
environments is still a difficult problem, for available computation time is
always limited, and for measurement include many outliers. To realize real time
algorithm for such an online process, we have developed an incremental version
of RANSAC algorithm by extending an efficient preemption RANSAC scheme. This
novel scheme named incremental RANSAC is able to find inlier hypotheses of
self-positions out of large number of outlier hypotheses contaminated by
outlier measurements.Comment: Offprint of ICRA2006 pape
Novel Adaptive Genetic Algorithm Sample Consensus
Random sample consensus (RANSAC) is a successful algorithm in model fitting
applications. It is vital to have strong exploration phase when there are an
enormous amount of outliers within the dataset. Achieving a proper model is
guaranteed by pure exploration strategy of RANSAC. However, finding the optimum
result requires exploitation. GASAC is an evolutionary paradigm to add
exploitation capability to the algorithm. Although GASAC improves the results
of RANSAC, it has a fixed strategy for balancing between exploration and
exploitation. In this paper, a new paradigm is proposed based on genetic
algorithm with an adaptive strategy. We utilize an adaptive genetic operator to
select high fitness individuals as parents and mutate low fitness ones. In the
mutation phase, a training method is used to gradually learn which gene is the
best replacement for the mutated gene. The proposed method adaptively balance
between exploration and exploitation by learning about genes. During the final
Iterations, the algorithm draws on this information to improve the final
results. The proposed method is extensively evaluated on two set of
experiments. In all tests, our method outperformed the other methods in terms
of both the number of inliers found and the speed of the algorithm
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
Many estimation problems in robotics, computer vision, and learning require
estimating unknown quantities in the face of outliers. Outliers are typically
the result of incorrect data association or feature matching, and it is common
to have problems where more than 90% of the measurements used for estimation
are outliers. While current approaches for robust estimation are able to deal
with moderate amounts of outliers, they fail to produce accurate estimates in
the presence of many outliers. This paper develops an approach to prune
outliers. First, we develop a theory of invariance that allows us to quickly
check if a subset of measurements are mutually compatible without explicitly
solving the estimation problem. Second, we develop a graph-theoretic framework,
where measurements are modeled as vertices and mutual compatibility is captured
by edges. We generalize existing results showing that the inliers form a clique
in this graph and typically belong to the maximum clique. We also show that in
practice the maximum k-core of the compatibility graph provides an
approximation of the maximum clique, while being faster to compute in large
problems. These two contributions leads to ROBIN, our approach to Reject
Outliers Based on INvariants, which allows us to quickly prune outliers in
generic estimation problems. We demonstrate ROBIN in four geometric perception
problems and show it boosts robustness of existing solvers while running in
milliseconds in large problems