Efficient probabilistic Range-Only SLAM

Blanco, Jose-Luis; Fernández‐Madrigal, Juan‐Antonio; González, J.

doi:10.1109/iros.2008.4650650

Cited by 74 publications

(54 citation statements)

References 14 publications

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“…As we stated above, the behavior of the robot is given by the boundary conditions, where the boundary is held at a potential defined by some function along the boundary, i.e., ∈∂Γ p( ) = f( ) for r r r (4) where ∂G is the borderline between the explored and unexplored space of the environment G. Wall-following exploring behavior is achieved just setting a low potential (f(r) = 0) in wall edges and their neighborhood. On the other hand, spacefilling exploring behavior is achieved setting low potential in unexplored concave regions.…”

Section: Path Planner Based On Boundary Value Problemsmentioning

confidence: 99%

“…The task of mapping and simultaneously self-localizing is difficult, because the robot needs to determine its pose based on a map that is being currently built and to map precisely the environment using the information on its pose. This problem is called Simultaneous Localization and Mapping (SLAM) and has attracted the attention of several researchers during last years 21,3,2,4 . Besides the position tracking problem, there are other challenging localization problems like the kidnapped robot problem and the wake-up robot problem.…”

Section: Introductionmentioning

confidence: 99%

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An improved particle filter for sparse environments

Prestes¹,

Ritt²,

Führ³

2009

J. Braz. Comp. Soc.

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Abstract:In this paper, we combine a path planner based on Boundary Value Problems (BVP) and Monte Carlo Localization (MCL) to solve the wake-up robot problem in a sparse environment. This problem is difficult since large regions of sparse environments do not provide relevant information for the robot to recover its pose. We propose a novel method that distributes particle poses only in relevant parts of the environment and leads the robot along these regions using the numeric solution of a BVP. Several experiments show that the improved method leads to a better initial particle distribution and a better convergence of the localization process.

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Section: Path Planner Based On Boundary Value Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved particle filter for sparse environments

Prestes¹,

Ritt²,

Führ³

2009

J. Braz. Comp. Soc.

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show abstract

“…Prior work has considered visual [1,2,3], range-only [4,5], and acoustic SLAM [6], as well as solutions based on multiple sensor modalities [7]. In [6], the authors propose a framework to simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot.…”

Section: Introductionmentioning

confidence: 99%

EchoSLAM: Simultaneous localization and mapping with acoustic echoes

Kreković

Dokmanić

Vetterli

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We address the problem of jointly localizing a robot in an unknown room and estimating the room geometry from echoes. Unlike earlier work using echoes, we assume a completely autonomous setup with (near) collocated microphone and the acoustic source. We first introduce a simple, easy to analyze estimator, and prove that the sequence of room and trajectory estimates converges to the true values. Next, we approach the problem from a Bayesian point of view, and propose a more general solution which does not require any assumptions on motion and measurement model of the robot. In addition to theoretical analysis, we validate both estimators numerically.

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“…The formulation was then extended to the 3D case with a spherical representation (Fabresse et al, 2013). Blanco et al (2008) follow the same idea but using a particle filter framework instead. In Boots and Gordon (2012), a spectral learning approach is proposed to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Range-Only Mapping via Sum-of-Squares Polynomials

et al. 2017

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This work presents a new approach for mapping static beacons given only range measurements. An original formulation using sum-of-squares and linear matrix inequalities is derived to test if a measurement is inconsistent with a bounding box containing the beacon position. By performing this test for each range measurement, it is possible to recursively eliminate incompatible boxes and find the smallest consistent box. The box search is done with a breadth-first search algorithm that recursively prunes inconsistent boxes and splits the others to narrow the estimation. The validity of the method is asserted via simulations and compared to other standard mapping methods. Different levels and types of noise are added to evaluate the performances of the algorithm. It resulted that the approach accommodates very well classical zero-mean white Gaussian noises by adaptating the ratio of tolerated outliers for the consistency check, but fails to handle additive biases.

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Efficient probabilistic Range-Only SLAM

Cited by 74 publications

References 14 publications

An improved particle filter for sparse environments

An improved particle filter for sparse environments

EchoSLAM: Simultaneous localization and mapping with acoustic echoes

Robust Range-Only Mapping via Sum-of-Squares Polynomials

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