65 research outputs found
Convergence and Consistency Analysis for A 3D Invariant-EKF SLAM
In this paper, we investigate the convergence and consistency properties of
an Invariant-Extended Kalman Filter (RI-EKF) based Simultaneous Localization
and Mapping (SLAM) algorithm. Basic convergence properties of this algorithm
are proven. These proofs do not require the restrictive assumption that the
Jacobians of the motion and observation models need to be evaluated at the
ground truth. It is also shown that the output of RI-EKF is invariant under any
stochastic rigid body transformation in contrast to based EKF
SLAM algorithm (-EKF) that is only invariant under
deterministic rigid body transformation. Implications of these invariance
properties on the consistency of the estimator are also discussed. Monte Carlo
simulation results demonstrate that RI-EKF outperforms -EKF,
Robocentric-EKF and the "First Estimates Jacobian" EKF, for 3D point feature
based SLAM
CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization
In this paper, we consider improving the efficiency of information-based
autonomous robot exploration in unknown and complex environments. We first
utilize Gaussian process (GP) regression to learn a surrogate model to infer
the confidence-rich mutual information (CRMI) of querying control actions, then
adopt an objective function consisting of predicted CRMI values and prediction
uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO).
The trade-off between the best action with the highest CRMI value
(exploitation) and the action with high prediction variance (exploration) can
be realized. To further improve the efficiency of GPBO, we propose a novel
lightweight information gain inference method based on Bayesian kernel
inference and optimization (BKIO), achieving an approximate logarithmic
complexity without the need for training. BKIO can also infer the CRMI and
generate the best action using BO with bounded cumulative regret, which ensures
its comparable accuracy to GPBO with much higher efficiency. Extensive
numerical and real-world experiments show the desired efficiency of our
proposed methods without losing exploration performance in different
unstructured, cluttered environments. We also provide our open-source
implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.Comment: Full version for the paper accepted by IEEE Robotics and Automation
Letters (RA-L) 2023. arXiv admin note: text overlap with arXiv:2301.0052
A stacked LSTM based approach for reducing semantic pose estimation error
© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories
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