A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

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.

Performance Evaluation of ROS-Based SLAM Algorithms for Handheld Indoor Mapping and Tracking Systems

“…The authors of [18] investigated the performance of Gmapping and Cartographer for autonomous vehicles. In [22], the authors proposed a Fast-SLAM which has a similar foundation as Gmapping since both use Rao-Blackwellised particle filter [13]. Both [18] and [22] concluded that their approach produced satisfactory results but only with the help of IMU and very reliable wheel odometry data.…”

“…In [22], the authors proposed a Fast-SLAM which has a similar foundation as Gmapping since both use Rao-Blackwellised particle filter [13]. Both [18] and [22] concluded that their approach produced satisfactory results but only with the help of IMU and very reliable wheel odometry data. Authors of [23] described a dual LiDAR system paired with IMU and wheel odometry to achieve mapping accuracy of up to 4cm.…”

Design and implementation of origami robot ROS-based SLAM and autonomous navigation

“…Zhang and collaborators, by comparing three SLAM algorithms and integrating a path planning analysis to assess their applicability in indoor rescue environments, have provided guidance for researchers in the construction of SLAM systems [ 29 ]. Lei improved the FastSLAM algorithm framework by introducing virtual particles as a global optimization objective and utilizing a particle swarm optimization approach [ 30 ]. Mu and colleagues proposed a graph-based multi-sensor SLAM (Simultaneous Localization and Mapping) method.…”

Improved Simultaneous Localization and Mapping Estimation using Crow Search Algorithm Based Particle Filter

“…Fast SLAM breaks down a SLAM problem into a robot positioning problem and a set of landmark estimation problems that are conditional on the robot status estimation [7]. So far, advanced versions of Fast SLAM have been offered by Lei et al [8], but all of them are based on one basic rule; as reported by Murphy [9], the representation as such is accurate due to the natural conditional independence in the SLAM problem. Fast SLAM uses a modified particle filter to estimate the posterior paths of the robot.…”