Bonus Round: Localization [Optional]

Additional Resources on Localization

Nice work reaching the end of the localization content! While you still have the project left to do here, we're also providing some additional resources and recent research on the topic that you can come back to if you have time later on.

Reading research papers is a great way to get exposure to the latest and greatest in the field, as well as expand your learning. However, just like the project ahead, it's often best to learn by doing - if you find a paper that really excites you, try to implement it (or even something better) yourself!

Optional Reading

All of these are completely optional reading - you could spend hours reading through the entirety of these! We suggest moving onto the project first so you have what you’ve learned fresh on your mind, before coming back to check these out.

We've categorized these papers to hopefully help you narrow down which ones might be of interest, as well as highlighted a couple key reads by category by including their Abstract section, which summarizes the paper.

Simultaneous Localization and Mapping (SLAM)

The below papers cover Simultaneous Localization and Mapping (SLAM) - which as the name suggests, combines localization and mapping into a single algorithm without a map created beforehand.

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age by C. Cadena, et. al.

Abstract: 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 within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. […]

Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality by S. Saeedi, et. al.

Abstract: Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. […]

Other Methods

The below paper from Udacity's founder Sebastian Thrun, while from 2002, is still relevant for many different methods of mapping used today in robotics.

Robotic Mapping: A Survey by S. Thrun

Abstract: This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research problems.