A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization

By John J. Leonard, H. Feder. 2000

332 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Ocean Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/c58d05af81eb9ee43b1ada346197b924c8ed11c1

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In Aerospace Engineering

In Ocean Engineering

In Oceanography

Citations in the corpus

Mapping and localization with RFID technology (2004, background): “However, due to the limited accuracy of the RFID sensors SLAM-techniques for range-only [14], [9], bearing-only [3] or range and bearing [5], [11], [15] cannot be applied directly to the data provided by the RFID system.”
Vision-based global localization and mapping for mobile robots (2005, background): “Leonard and Feder [ 18 ] proposed decoupled stochastic mapping by representing the environment in terms of multiple globally-referenced submaps.”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, background): “This limits critically the number of features that can be mapped (for a recent attempt to escape this limitation using hierarchies of maps, see Leonard and Feder 1999).”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “Due to this graceful increase in complexity, Kalman filters can be applied to the simultaneous localization and map building problem (SLAM), where they estimate full posteriors over both robot positions and landmark positions (typically consisting of hundreds of dimensions) (Cox and Leonard 1994; Di”
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]).”
Robotic mapping: a survey (2003, background): “Recent research has led to a range of extensions that can handle larger number of features, by decomposing the problem into multiple smaller ones [56, 38].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “Sonars were among the ﬁrst EKF SLAM approaches for underwater applications [20][21].”
Coordinated multi-robot exploration (2005, background): “Now suppose the robots can communicate again and exchange their maps.”
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks (2002, background): “Decoupled stochastic mapping reduces this computational burden by dividing the environment into multiple overlapping submap regions, each with its own stochastic map (Leonard and Feder 1999).”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “Paskin (2003) included an excellent comparison of these techniques.”
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003, background): “Others restrict the update exclusively to local maps [12], hence operate in constant time (assuming known data association).”
Map building with mobile robots in dynamic environments (2003, background): “In the literature, the mobile robot mapping problem is often referred to as the simultaneous localization and mapping problem (SLAM) [5] [7] [13] [17] [14] [10] [19].”
An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements (2003, background): “In the literature, the mobile robot mapping problem is often referred to as thesimultaneous localization and mapping problem (SLAM) [5], [6], [9], [12], [13], [19].”
Optimization of the simultaneous localization and map-building algorithm for real-time implementation (2001, background): “[19], addressed the computational issues splitting the global map into a number of sub-maps, each with their own vehicle track.”
Real-time simultaneous localisation and mapping with a single camera (2003, methodology): “…uncertainty in the coupled estimates of robot and map feature positions, combined with various techniques for reducing computational complexity in large maps, have shown great success in enabling robots to estimate their locations accurately and robustly over large movement areas [7] [13] .”
Probabilistic Algorithms in Robotics (2000, background): “This critically limits the number of features that can be mapped (see [59] for a recent attempt to escape this limitation using hierarchies of maps).”