Unscented FastSLAM Research Articles

Adaptive SLAM Methodology Based on Simulated Annealing Particle Swarm Optimization for AUV Navigation

Simultaneous localization and mapping (SLAM) is crucial and challenging for autonomous underwater vehicle (AUV) autonomous navigation in complex and uncertain ocean environments. However, inaccurate time-varying observation noise parameters may lead to filtering divergence and poor mapping accuracy. In addition, particles are easily trapped in local extrema during the resampling, which may lead to inaccurate state estimation. In this paper, we propose an innovative simulated annealing particle swarm optimization-adaptive unscented FastSLAM (SAPSO-AUFastSLAM) algorithm. To cope with the unknown observation noise, the maximum a posteriori probability estimation algorithm is introduced into SLAM to recursively correct the measurement noise. Firstly, the Sage–Husa (SH) based unscented particle filter (UPF) algorithm is proposed to estimate time-varying measurement noise adaptively in AUV path estimation for improving filtering accuracy. Secondly, the SH-based unscented Kalman filter (UKF) algorithm is proposed to enhance mapping accuracy in feature estimation. Thirdly, SAPSO-based resampling is proposed to optimize posterior particles. The random judgment mechanism is used to update feasible solutions iteratively, which makes particles disengage local extreme values and achieve optimal global effects. The effectiveness and accuracy of the proposed algorithm are evaluated through simulation and sea trial data. The average AUV navigation accuracy of the presented SAPSO-AUFastSLAM method is improved by 18.0% compared to FastSLAM, 6.5% compared to UFastSLAM, and 5.9% compared to PSO-UFastSLAM.

A Variational Bayesian-Based Simultaneous Localization and Mapping Method for Autonomous Underwater Vehicle Navigation

Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods.

An Improved Adaptive Unscented FastSLAM with Genetic Resampling

The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, an improved adaptive unscented FastSLAM with genetic resampling is proposed. Specifically, the adaptive unscented Kalman filter (IAUKF) algorithm is improved as importance sampling of particle filter, where the adaptive factor is used to improve the tracking ability of system and the Huber cost function is constructed to decrease the measurement covariance. Next, the process noise and the measurement noise are assessed by a time varying estimator. Moreover, the resampling in particle filter is carried out by an improved genetic algorithm (GA). Finally, the improved adaptive unscented FastSLAM (IAUFastSLAM) is proposed to complete robot tracking. The proposed algorithm has good tracking performance and obtains reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.

An improved H-infinity unscented FastSLAM with adaptive genetic resampling

The FastSLAM is a typical tracking algorithm for SLAM, but it often suffers from the low tracking accuracy. To mitigate the problem, an improved H-Infinity unscented FastSLAM (IHUFastSLAM) with adaptive genetic resampling is proposed in this paper. Specifically, the H-Infinity unscented Kalman filter algorithm is improved using an adaptive factor and is employed as importance sampling in particle filter. Next, the process noise and the measurement noise are estimated by a time varying noise estimator. Moreover, an adaptive genetic algorithm is used to complete the resampling of particle filter. Finally, the improved H-Infinity UFastSLAM with adaptive genetic resampling is proposed to complete robot tracking. The proposed algorithm can track robot with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.

A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor

This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore, the state estimation would converge to the true value rapidly and the accuracy of system state estimation would be improved eventually. The results of simulations and practical tests show that strong tracking unscented FastSLAM (STUFastSLAM) has a significant improvement in accuracy and robustness.

Adaptive square-root transformed unscented FastSLAM with KLD-resampling

ABSTRACTThe FastSLAM relies on particles sampled from the proposal distribution of underlying Rao–Blackwellized particle filter, and its performance is significantly influenced by the quality and quantity of the particles. In this paper, a new improved FastSLAM is proposed based on transformed unscented Kalman filter (TUKF) and Kullback–Leibler distance (KLD) resampling method. In the proposed algorithm, a square-root extension of TUKF is used to calculate the proposal distribution and to generate credible particles. In addition, during the resampling process, the minimum required number of particles is determined adaptively by bounding the KLD error between the sample-based approximation and true posterior distribution of the robot state. Both numerical simulations and real-world dataset experiments are used to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm achieves higher estimation accuracy and computational efficiency than conventional approaches.

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor's observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.

Improved Unscented FastSLAM Using Geometric Information of Particles

Improved Unscented FastSLAM Using Geometric Information of Particles

Unscented H-infinity filtering based simultaneous localization and mapping with evolutionary resampling

Unscented FastSLAM (UFastSLAM) is a framework for simultaneous localization and mapping (SLAM). However, UFastSLAM is inconsistent over time due to the loss of particle diversity that caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, in this paper, H-infinity UFastSLAM (HUFastSLAM) with evolutionary resampling is proposed. The proposed method can work in unknown statistical noise and does not require a prior knowledge about the of the noise statistics. In addition, to increase diversity, the resampling process is done based on the differential evolution (DE). The proposed algorithm is evaluated on a benchmark dataset. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm in different situation.

Autonomous navigation based on unscented-FastSLAM using particle swarm optimization for autonomous underwater vehicles

Simultaneous localization and mapping (SLAM) is the crucial prerequisite for mobile robots to accomplish autonomy. In this paper, PSO-UFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the particle swarm optimization (PSO) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the robot poses and features. Furthermore, to prevent the particles degeneracy and impoverishment, PSO is adapted to optimize particles. The proposed method is applied on our own research platform, autonomous underwater vehicle (AUV), through sea trials in Tuandao Bay. The results of simulation and sea trial reveal that PSO-UFastSLAM has better accuracy and effectiveness in terms of estimation of robot and features while compared with UFastSLAM and standard FastSLAM.

Vision-Based Semantic Unscented FastSLAM for Indoor Service Robot

This paper proposes a vision-based Semantic Unscented FastSLAM (UFastSLAM) algorithm for mobile service robot combining the semantic relationship and the Unscented FastSLAM. The landmark positions and the semantic relationships among landmarks are detected by a binocular vision. Then the semantic observation model can be created by transforming the semantic relationships into the semantic metric map. Semantic Unscented FastSLAM can be used to update the locations of the landmarks and robot pose even when the encoder inherits large cumulative errors that may not be corrected by the loop closure detection of the vision system. Experiments have been carried out to demonstrate that the Semantic Unscented FastSLAM algorithm can achieve much better performance in indoor autonomous surveillance than Unscented FastSLAM.

Conjugate Unscented FastSLAM for Autonomous Mobile Robots in Large-Scale Environments

Map learning and self-localization based only on a perception of an environment’s structure are fundamental cognitive capacities required for intelligent robot’s to realize true autonomy. Simultaneous localization and mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robot’s pose with the built map. While the Rao-Blackwellized particle filter (RBPF) is a popular SLAM technique, it tends to accumulate errors introduced by inaccurate linearization of the SLAM nonlinear function. Accordingly, RBPF-SLAM will usually fail to close large loops when applied to large-scale environments. To overcome this drawback, a new Jacobian-free RBPF-SLAM algorithm is derived in this paper. The main contribution of the algorithm lies in the utilization of the 5th-order conjugate unscented transform, which calculates the SLAM transition density up to the 5th order, to give a better distribution of the particle filter and discover local features and landmarks. The performance of the proposed SLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in both indoor and outdoor experiments. The results verify that the proposed algorithm improves the SLAM performance in large-scale environments.

Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.

Unscented FastSLAM: A Robust and Efficient Solution to the SLAM Problem

The Rao-Blackwellized particle filter (RBPF) and FastSLAM have two important limitations, which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. These can make the filter inconsistent. Another challenge is to reduce the number of particles while maintaining the estimation accuracy. This paper provides a robust new algorithm based on the scaled unscented transformation called unscented FastSLAM (UFastSLAM). It overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations. This approach improves the filter consistency and state estimation accuracy, and requires smaller number of particles than the FastSLAM approach. Simulation results in large-scale environments and experimental results with a benchmark dataset are presented, demonstrating the superiority of the UFastSLAM algorithm.