Robust Mapping and Localization in Indoor Environments Using Sonar Data

By Juan D. Tardós, José Neira, Paul M. Newman, John J. Leonard. Int. J. Robotics Res., 2002

619 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/1f3e3872e1c28d76da663164c431929aeacc57a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS (2006, background): “SLAM has been especially succesful in indoor structured environments [9, 7, 20].”
Vision-based global localization and mapping for mobile robots (2005, background): “Tardos et al. [28] proposed sequential map joining that builds a sequence of independent limited-size stochastic maps and joins them in a globally consistent way; their method can handle loop closure.”
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM (2007, background): “, [16], [17], [18]), where the robot orientation uncertainties in each local map are kept very small, have the potential to improve consistency.”
Hierarchical SLAM: real-time accurate mapping of large environments (2005, methodology): “All cited methods work on a single absolute map representation and confront divergence due to nonlinearities as uncertainty increases when mapping large areas [7].”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, methodology): “Another recent technique called local map sequencing [127] performs local map construction and registration in a manner virtually identical to the CLSF.”
Robust Range-Only Beacon Localization (2004, methodology): “Hough transforms have previously been used to classify sonar returns from point and line features by Tardós [6].”
Factor Graphs for Robot Perception (2017, background): “Many attempts were made to extending the filtering approach to cope with larger-scale environments [26, 52, 130, 91, 184, 188, 92], but filtering itself was shown to be inconsistent [105] when applied to the inherently nonlinear SLAM problem.”
An Atlas framework for scalable mapping (2003, methodology): “where J1 (·, ·) and J2 (·, ·) are the Jacobians of the transformation composition operators [15].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “tion of each subgraph, with a global reﬁnement. The corresponding approaches are often referred to as submapping algorithms, an idea that dates back to the initial attempts to tackle large-scale maps [30, 99, 296]. Ni et al. [230] present an exact submapping approach for factor graph optimization, a”
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing (2006, background): “…2000; Dissanayake et al. 2001; Castellanos et al. 1999; Julier and Uhlman 2001b; Leonard and Feder 2001; Guivant and Nebot 2001; Paskin 2003; Leonard and Newman 2003; Bosse et al. 2003; Thrun et al. 2004; Guivant et al 2004; Tardos et al. 2002; Williams et al. 2002; Rodriguez-Losada et al. 2004).”
Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework (2004, methodology): “Efficient strategies for SLAM with feature-based representations and Gaussian representation of error include postponement (Davison 1998), decoupled stochastic mapping (DSM; Leonard and Feder 2001), the compressed filter (Guivant and Nebot 2001), sequential map joining (Tardós et al. 2002), the cons”
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures (2006, background): “Authors such as Guivant and Nebot (2001), Williams (2001), Bailey (2002) and Tardós et al. (2002) report data structures for minuting the relative displacement between submaps, which are easily mapped to information theoretic concepts.”
FastSLAM: a factored solution to the simultaneous localization and mapping problem (2002, methodology): “[65] developed a technique called Local Map Sequencing for building maps of indoor environments using sonar data.”
Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, methodology): “Alternative methods based on decomposition into submaps, such as the sequential map joining techniques described in Tardós et al. (2002) and Williams, Dissanayake, and Durrant- Whyte (2002) can achieve the same rate of convergence as the full EKF solution, but incur an O(N”
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003, background): “It also limits the applicability of SLAM algorithms to problems with ambiguous landmarks, which induces a data association problem [2; 22].”