Optimization of the simultaneous localization and map-building algorithm for real-time implementation

By José E. Guivant, Eduardo Mario Nebot. IEEE Trans. Robotics Autom., 2001

894 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/60467e9034a26591161082259486e4b6651c3091

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 Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing (2006, methodology): “…environments (Newman 1999; Deans and Hebert 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…”
Data association in stochastic mapping using the joint compatibility test (2001, methodology): “SCNN is an algorithm, quadratic with the size of the map: for each of the measurements, SCNN requires evaluating compatibility with themap features, , and updating the stochastic map, (recent works address the reduction of this complexity [22], [23]).”
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, background): “This shortcoming has long been recognized by the research community [6, 8, 13].”
Navigation Strategies for Exploring Indoor Environments (2002, background): “Such an operation, which is not implemented in our system, can be found in recent SLAM systems (Dissanayake et al. 2001; Durrant-Whyte, Dissanayake, and Gibbens 2000; Guivant and Nebot 2001).”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “This solution extracts features or landmarks in the environment and keeps a list of them as a map (Smith, Self, & Cheeseman, 1987; Guivant & Nebot, 2001).”
Hierarchical SLAM: real-time accurate mapping of large environments (2005, methodology): “Several current methods address the computational complexity problem by working on a limited region of the map.”
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003, result): “Furthermore , we observe experimentally that our new FastSLAM algorithm, even with a single particle, yields significantly more accurate results on a challenging real-world benchmark [7] than the previous version of the algorithm.”
The SLAM problem: a survey (2008, methodology): “Compressed Extended KF (CEKF) [ 23 ] - reduced uncertainty - require very robust features - reduction of memory usage - data association problem - handle large areas - require multiple map merging - increase map consistency”
Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, methodology): “We specifically compare our new approach to the EKF solution, using a benchmark data set collected in an outdoor environment (Dissanayake et al. 2001; Guivant and Nebot 2001).”
An Atlas framework for scalable mapping (2003, methodology): “Efficient strategies for SLAM with feature-based representations and Gaussian representation of error include postponement [2], decoupled stochastic mapping [10], the compressed filter [6], sequential map joining [15], the constrained local submap filter [18], and sparse extended information filters”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, methodology): “Nevertheless, performing decorrelation after each observation would result in a very conservative SLAM estimate, and the RLR achieves its near-optimal convergence properties by employing the compressed EKF (CEKF) [65] as its estimation method.”
Cooperative concurrent mapping and localization (2002, methodology): “Our work uses a feature-based representation [21], [22], [ 23 ].”
Simultaneous localization and mapping: part I (2006, background): “Guivant and Nebot [21] pioneered the application of SLAM in very large outdoor environments (see Figure 7).”
Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing (2006, background): “Furthermore, several authors have considered the problem of simultaneous mapping and localization (SLAM) in an outdoor environment [4], [6], [27].”
Simultaneous localization and mapping (SLAM): part II (2006, methodology): “This approach is taken by the compressed EKF (CEKF) [21] and the postponement algorithm [28].”
Exactly Sparse Delayed-State Filters for View-Based SLAM (2006, neutral): “proaches include submaps [25]–[27], postponement [28]–[30],”
Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework (2004, methodology): “Results for this data set were first published by Guivant and Nebot (2001), using the compressed filter, an efficient method for largescale SLAM based on the Kalman filter.”