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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
TLDR
What is now the de-facto standard formulation for SLAM is presented, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers.
Data association in stochastic mapping using the joint compatibility test
TLDR
This paper proposes a new measurement of the joint compatibility of a set of pairings that successfully rejects spurious matchings and shows experimentally that this restrictive criterion can be used to efficiently search for the best solution to data association.
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
TLDR
DynaSLAM is a visual SLAM system that, building on ORB-SLAM2, adds the capabilities of dynamic object detection and background inpainting, and outperforms the accuracy of standard visualSLAM baselines in highly dynamic scenarios.
Robust Mapping and Localization in Indoor Environments Using Sonar Data
TLDR
A perceptual grouping process that permits the robust identification and localization of environmental features from the sparse and noisy sonar data, and a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way.
Hierarchical SLAM: real-time accurate mapping of large environments
TLDR
A close to optimal loop closing method is proposed that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop.
Mapping Large Loops with a Single Hand-Held Camera
This paper presents a method for Simultaneous Localization and Mapping (SLAM) relying on a monocular camera as the only sensor which is able to build outdoor, closedloop maps much larger than
Robust Loop Closing Over Time
TLDR
This paper proposes a novel consistency based method to extract the loop closure regions that agree both among themselves and with the robot trajectory over time, and assumes that the contradictory loop closures are inconsistent among itself and withThe robot trajectory.
Divide and Conquer: EKF SLAM in O(n)
TLDR
This paper describes Divide and Conquer SLAM, which is an EKF SLAM algorithm in which the computational complexity per step is reduced from O(n 2) to O( n), and the total cost of SLAM is reduced to O3, from O3 to O2.
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