Adaptive Multimodal Localisation Techniques for Mobile Robots in Unstructured Environments : A Review

Abstract

Mobile robots can be integrated as an entity in the new paradigm of the Internet of Things (IoT) and can be instrumental in extending sensing and manipulation capabilities to remote environments where the installation of sensor networks is unfeasible. Many anticipated applications of autonomous mobile robots require for them to navigate in diverse complex environments without support from exterior infrastructures. To perform this on-board navigation, the robot must make use of the available sensor technologies and fuse the most reliable data respective to the present environment in an adaptive manner. This paper will review recent efforts to develop onboard navigation systems which can seamlessly transition between outdoor and indoor environments and different terrains seamlessly. The methodologies surveyed include visual SLAM, Odometry and Place Recognition. An overview of the state-of-the-art is provided with a focus on approaches which are adaptive to dynamic sensor uncertainty, dynamic objects and dynamic scenes. In addition, the paper also provides an analysis of the most common sensor modalities and the factors affecting sensor uncertainty for the same.