A Study On Mobile Robot Simultaneous Localization And Map Building

Autonomous Ground Vehicle (AGV) is an intelligent mobile robot, which can run autonomously, and continuously in real-time indoor or outdoor. The development of AGV has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries. Autonomous navigation is a fundamental problem for Autonomous Ground Vehicle.The simultaneous localization and map building (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The SLAM problem has attracted a lot of researchers with a broad rang of interests and applications since 1990s. A solution to the SLAM problem has been seen as a "holy grail" for the mobile robotics community as it would provide the means to make a robot truly autonomous. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementation of SLAM methods. SLAM has been implemented in a number of different domains from indoor robots, to outdoor, underwater and airborne systems.This dissertation is focused on the SLAM problem. Several improved methods and novel solutions are presented in order to improve consistency and computational efficiency, and additionally extend SLAM application domains. The main content of this dissertation include the following aspects:1. Through the analysis of uncertainty, the correlation in SLAM problem is studied. It's well-known that the correlation between features is actually the critical part of the SLAM problem. Maintaining and renewing this correlation information brings a huge computation burden. Furthermore, on the basis of having carried out deep analysis on correlation, a new feature sparse tactic named correlation priority is brought forward, which may use less features which having strong correlation to cut down large amount of the computation burden, and the computation error of this method can compare with that of some general traditional methods.2. In SLAM problem, motion and measurement models are usually of a very high nonlinear nature. An approach is designed which aims to avoid the analytical linearization based on Taylor-series expansion of both motion and measurement models by using scaled unscented transformation.3. The inconsistency problem of the Rao-Blackwellised particle filter (RBPF) SLAM algorithm is analyzed by using the normalized estimation error square (NEES). The result shows that it is sample impoverishment of particle filter which cause the inconsistency. So it is necessary to reduce the impact of resampling. Auxiliary particle filter and regularized particle filter are used to improve the RBPF SLAM resampling step in order to obtain consistent RBPF SLAM.4. In order to avoid the sample impoverishment problem of particle filter, a new particle filter SLAM algorithm is proposed, which is based on the marginal particle filter and using unscented Kalman filter (UKF) to generate proposal distributions. The underlying algorithm operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Additionally, UKF can reduce linearization error and gain accurate proposal distributions. Compare with the conventional particle filter SLAM methods, the new algorithm increases the number of effective particles and reduces variance of particles weight effectively. Also, it is consistent owing to the better particle diversity. As a result, it does not suffer from some shortcomings of existing particle methods for SLAM and has distinct superiority.5. In order to enhance the performance of sparse extended information filter (SEIF) SLAM algorithm, a novel sparsification rule is brought forward. The rule takes into account the observation information of sparsification time. The correlation can be observed globally and the features which have the strongest correlation are reserved. Therefore, the new algorithm gets higher estimation precision and more consistent results than the conventional SEIF algorithm by increasing no computation burden. Furthermore, an integrated sparsification rale is brought forward. The application environments are expanded.6. A new data association algorithm named fast JCBB (FJCBB) is proposed by giving a bound of joint compatibility pairings based on joint compatibility branch and bound (JCBB) algorithm. FJCBB has the same association performance with JCBB. Furthermore, FJCBB's computational cost increases very slowly as the number of observations increases, this character makes FJCBB more feasible than JCBB. When the number of observations is large, FJCBB will also be adequately fast for real time implementation.7. The relocation problem is studied and an improved random sampling (RS) relocation algorithm is proposed. In RS algorithm the search part is a component exponential in the number of observations. When the number of observations is large, its computational cost increases rapidly. Therefore, the algorithm is improved by using FJCBB algorithm. The improved algorithm's search part is linear time proportional to the number of observations. Furthermore, generally an empirical threshold of associated pairings is used to prevent false positives in relocation problem. When the number of pairings is lower than the threshold, a new method named motion constrains is brought forward to verify the relocation reliability. If it's reliable, the relocation results will be accepted.A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.