AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Poboljšani FastSLAM2.0 algoritam korištenjem ANFIS-a i PSO-a

FastSLAM2.0 is a framework for simultaneous localization of robot using a Rao-Blackwellized particle filter (RBPF). One of the problems of FastSLAM2.0 relates to the design of RBPF. The performance and quality of the estimation of RBPF depends heavily on the correct a priori knowledge of the process and measurement noise covariance matrices that are in most real-life applications unknown. On the other hand, an incorrect a priori knowledge may seriously degrade their performance. This paper presents an intelligent RBPF to solve this problem. In this method, two adaptive Neuro-Fuzzy inference systems (ANFIS) are used for tuning the process and measurement noise covariance matrices and for increasing acuuracy and consistency. In addition, we use particle swarm optimization (PSO) to optimize the performance of sampling. Experimental results demonstrate that the proposed algorithm is effective.FastSLAM2.0 je algoritam za istodobnu lokalizaciju robota i kartiranje prostora koji koristi Rao-Blackwell verziju čestičnog filtra (RBPF). Jedan od problema FastSLAM2.0 algoritma je u dizajnu samog RBPF-a. Performanse i kvaliteta estimacije RBPF-a značajno ovisi o apriori poznavanju procesa i matrica kovarijanci mjernog šuma koje su za većinu procesa iz stvarnog svijeta nepoznate. S druge strane pogrešno pretpostavka može značajno narušiti performanse. Ovaj rad predstavlja inteligentnu verziju RBPF-a koja rješava ovaj problem. Predstavljena metoda koristi dva adaptivna neizrazito-neuronska sustava (ANFIS) za podešavanje matrica kovarijanci procesnog i mjernog šuma čime se povećava točnost i konzistencija RBPF algoritma. Također koristi se i optimizacija roja čestica (PSO) za optimiziranje performansi otipkavanja. Eksperimentalni rezultati pokazuju efikasnost predloženog algoritma

Resampling Methods for Particle Filtering: Identical Distribution, a New Method, and Comparable Study

Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial (Li et al., 2015). First, identical distribution (ID) is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent metrics including the (symmetrical) Kullback-Leibler divergence, Kolmogorov-Smirnov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive understanding of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations

다중 로봇 SLAM을 위한 상관관계 기반 지도병합 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이범희.Multi-robot simultaneous localization and mapping (SLAM) is an advanced technique used by multiple robots and autonomous vehicles to build up a collective map within an unknown environment, or to update a collective map within a known environment, while at the same time keeping track of their current location. The collective map is obtained by merging individual maps built by different multiple robots exploring the environment. When robots do not know their initial poses one another, the problem of map merging becomes challenging because the robots have different coordinate systems. If robot-to-robot measurements are not available, the problem of map merging becomes more challenging because the map transformation matrix (MTM) among robots cannot be computed directly. This dissertation presents novel map merging techniques based on the analysis of the correlation among the individual maps, which do not need the knowledge of the relative initial poses of robots and the robot-to-robot measurements. After the cross-correlation function among the spectrometric or tomographic information extracted from the individual maps is generated, the MTM is computed by taking the rotation angle and the translation amounts corresponding to the maximum cross-correlation values. The correlation-based map merging techniques with spectral information presented in this dissertation are the extensions of a conventional map merging technique. One extension is spectrum-based feature map merging (SFMM), which extracts the spectral information of feature maps from virtual supporting lines and computes the MTM by matching the extracted spectral information. The other extension is enhanced-spectrum-based map merging (ESMM), which enhances grid maps using the locations of visual objects and computes the MTM by matching the spectral information extracted from the enhanced grid maps. The two extensions overcome successfully the limitation of the conventional map merging technique. The correlation-based map merging technique with tomographic information is a new map merging technique, which is named tomographic map merging (TMM). Since tomographic analysis can provide more detailed information on grid maps according to rotation and translation than spectral analysis, the more accurate MTM can be computed by matching the tomographic information. The TMM was tested on various pairs of partial maps from real experiments in indoor and outdoor environments. The improved accuracy was verified by showing smaller map merging errors than the conventional map merging technique and several existing map merging techniques.Chapter 1 Introduction 1.1 Background and motivation 1.2 Related works 1.3 Contributions 1.4 Organization Chapter 2 Multi-Robot SLAM and Map Merging 2.1 SLAM using Particle Filters 2.2 Multi-Robot SLAM (MR-SLAM) 2.2.1 MR-SLAM with Known Initial Correspondences 2.2.2 MR-SLAM with Unknown Initial Correspondences 2.3 Map Merging Chapter 3 Map Merging based on Spectral Correlation 3.1 Spectrum-based Map Merging (SMM) 3.2 Spectrum-based Feature Map Merging (SFMM) 3.2.1 Overview of the SFMM 3.2.2 Problem Formulation for the SFMM 3.2.3 Virtual Supporting Lines (VSLs) 3.2.4 Estimation of Map Rotation with Hough Spectra 3.2.5 Rasterization of Updated Feature Maps with VSLs 3.2.6 Estimation of Map Displacements 3.3 Enhanced-Spectrum-based Map Merging (ESMM) 3.3.1 Overview of the ESMM 3.3.2 Problem Formulation for the ESMM 3.3.3 Preprocessing – Map Thinning 3.3.4 Map Enhancement 3.3.5 Estimation of Map Rotation 3.3.6 Estimation of Map Translations Chapter 4 Map Merging based on Tomographic Correlation 4.1 Overview of the TMM 4.2 Problem Formulation for the TMM 4.3 Extraction of Sinograms by the Radon Transform 4.4 Estimation of a Rotation Angle 4.5 Estimation of X-Y Translations Chapter 5 Experiments 5.1 Experimental Results of the SFMM 5.2 Experimental Results of the ESMM 5.2.1 Results in a Parking Area 5.2.2 Results in a Building Roof 5.3 Experimental Results of the TMM 5.3.1 Results in Indoor Environments 5.3.2 Results in Outdoor Environments 5.3.3 Results with a Public Dataset 5.3.4 Results of Merging More Maps 5.4 Comparison among the Proposed Techniques 5.5 Discussion Chapter 6 Conclusions BibliographyDocto