Proceedings ArticleDOI
Invariant EKF based 2D Active SLAM with Exploration Task
Mengya Xu,Yang Song,Yongbo Chen,Shoudong Huang,Qi Hao +4 more
- pp 5350-5356
TLDR
In this paper, the authors proposed to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM result applying the selected control actions are computed using RIEkF algorithms.Abstract:
Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm3.read more
Citations
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Journal ArticleDOI
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers
Julio A. Placed,Jared Strader,Henry Carrillo,Nikolay Atanasov,Vadim Indelman,Luca Carlone,José A. Castellanos +6 more
TL;DR: This work surveys the state-of-the-art in active SLAM and takes an in-depth look at the open challenges that still require attention to meet the needs of modern applications, including reproducible research, active spatial perception, and practical applications.
Journal ArticleDOI
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers
TL;DR: Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment as mentioned in this paper .
Journal ArticleDOI
Camera view planning based on generative adversarial imitation learning in indoor active exploration
TL;DR: In this paper , a novel active vSLAM framework with camera view planning based on generative adversarial imitation learning (GAIL) is proposed to actively adjust the orientation of the camera during robot motion.
Journal ArticleDOI
Active SLAM: A Review On Last Decade
TL;DR: In this paper , a review of active SLAM (A-SLAM) research conducted in the last decade is presented, which highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches.
References
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Journal ArticleDOI
iSAM2: Incremental smoothing and mapping using the Bayes tree
TL;DR: The Bayes tree is applied to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps.
Journal ArticleDOI
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
TL;DR: It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates.
Journal ArticleDOI
Adaptive Mobile Robot Navigation and Mapping
TL;DR: An implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features is described.
Proceedings ArticleDOI
An experiment in integrated exploration
TL;DR: To provide a uniform basis of comparison of localization quality between different locations, a "localizability" metric is introduced, based on the estimate of the lowest vehicle pose covariance attainable from a given location.
Journal ArticleDOI
The Invariant Extended Kalman Filter as a Stable Observer
Axel Barrau,Silvere Bonnabel +1 more
TL;DR: In this article, the authors analyzed the convergence aspects of the invariant extended Kalman filter (IEKF) when the latter is used as a deterministic nonlinear observer on Lie groups, for continuous-time systems with discrete observations.