Abstract
The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments.
Highlights
Owing to the rapid development of autonomous mobile robots, simultaneous localisation and mapping (SLAM) [1] has emerged as a crucial technology in a great many applications, such as self-driving, exploration, and navigation
We simulated FastSLAM, particle swarm optimisation (PSO)-FastSLAM (FastSLAM based on PSO with particle weights), AGA-FastSLAM (FastSLAM based on AGA-Resampling), and IFastSLAM with the same simulation parameters in order to verify the superiority of the improved algorithm
The results reveal that the map generated by IFastSLAM has fewer defects and higher precision, while the maps generated by the PSO-FastSLAM and AGA-FastSLAM algorithms are of low quality
Summary
Owing to the rapid development of autonomous mobile robots, simultaneous localisation and mapping (SLAM) [1] has emerged as a crucial technology in a great many applications, such as self-driving, exploration, and navigation. SLAM can help a robot acquire information on the surrounding environment through a self-positioning process in an unknown environment, and gradually builds an incremental map. The robot performs the SLAM process iteratively in order to eliminate uncertain factors and to estimate the position accurately. Due to the high accuracy and high frequency of lidar, it is very suitable for observation of complex environments. SLAM based on lidar is widely used in practical robot applications. Lidar-based SLAM technologies include 2D lidar SLAM and 3D lidar SLAM
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