Consistent Map Building Based on Sensor Fusion for Indoor Service Robot

Abstract

Consider the actual applications of an intelligent service robot (ISR), it is expected that an ISR will not only autonomously estimate the environment structure but also detect the meaningful symbols or signs in the building it services. For example, an ISR has to locate all the docking stations for recharging itself. For an ISR to lead a customer in the department store to any location such as the toy department or the nearest restroom, it must have the essential recognizing and guiding ability for its service. For this purpose, to carry out an applicable self-localization and map building technique for the indoor service robot becomes important and desirable. In recent years the sensing and computing technology have made tremendous progress. Various simultaneous localization and mapping (SLAM) techniques have been implemented. The principle of SLAM is derived from Bayesian framework. The EKF-SLAM (DurrantWhyte & Bailey, 2006) is based on robot state estimation. However, EKF-SLAM will fail in large environments caused by inconsistent estimation problem from the linearization process (Rodriguez-Losada et al., 2006) (Bailey et al., 2006) (Shoudong & Gamini, 2007). A full SLAM algorithm is using sequential Monte Carlo sampling method to calculate robot state as particle filter (Montemerlo et al., 2002) (Montemerlo et al., 2003). But the technique will grow exponentially with the increase of dimensions of the state space. Another full scan matching method is suitable for the environment reconstruction (Lu & Milios, 1997) (Borrmann et al., 2008). But the pose variable will also grow enormously depending on the sampling resolution. Based on the practical needs of a service robot patrol in the building, it is desirable to construct an information map autonomously in a unitary SLAM process. This chapter investigates a consistent map building by laser rangefinder. Firstly, the Covariance Intersection (CI) method is utilized to fuse the robot pose for a robust estimation from wheel encoder and laser scan match. Secondly, a global look up table is built for consistent feature association and a global fitness function is also generated. Finally, the Particle Swarm Optimization (PSO) method is applying to solve the optimal alignment problem. From the proposed method, a consistent map in a unitary localization and mapping process via the sensor fusion and optimal alignment methodology has been constructed and implemented 12