Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for Intelligent Service Robotics

Abstract

Intelligent service robot development is an important and critical issue for human community applications. With the diverse and complex service needs, the perception and navigation are essential subjects. This investigation focuses on the synergistic fusion of multiple sensors for an intelligent service robot that not only performs self-localization and mapping but also detects moving objects or people in the building it services. First of all, a new augmented approach of graph-based optimal estimation was derived for concurrent robot postures and moving object trajectory estimate. Moreover, all the moving object detection issues of a robot's indoor navigation are divided and conquered via multisensor fusion methodologies. From bottom to up, the estimation fusion methods are tactically utilized to get a more precise result than the one from only the laser ranger or stereo vision. Furthermore, for solving the consistent association problem of moving objects, a covariance area intersection belief assignment is applied for motion state evaluation and the complementary evidences such as kinematics and vision features are both synergized together to enhance the association efficiency with the evidence fusion method. The proof of concept with experiments has been successfully demonstrated and analyzed.