The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing
progress over the last few decades and has generated considerable interest in the …

This paper is intended to pave the way for new researchers in the field of robotics and
autonomous systems, particularly those who are interested in robot localization and …

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation
enabling a better understanding of existing graphical model inference algorithms and their …

Robots that can move autonomously and can make intelligent decisions by perceiving their
environments and surrounding objects are known as autonomous mobile robots. Such …

RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB
images along with per-pixel depth information. In this paper we investigate how such …

RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of
their data and the recent development of low-cost sensors have combined to present an …

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel
approach to the simultaneous localization and mapping problem that is based on fast …

Many successful indoor mapping techniques employ frame-to-frame matching of laser scans
to produce detailed local maps as well as the closing of large loops. In this paper, we …

This chapter provides a comprehensive introduction in to the simultaneous localization and
mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main …