For long-duration operations in GPS-denied environments, accurate and repeatable waypoint navigation is an essential capability. While simultaneous localization and mapping (SLAM) works well for single-session operations, repeated, multi-session operations require robots to navigate to the same spot(s) accurately and precisely each and every time. Localization and navigation errors can build up from one session to the next if they are not accounted for. Localization using a global reference map works well, but there are no publicly available packages for quickly building maps and navigating with them. We propose a new architecture using a combination of two publicly available packages with a newly released package to create a fully functional multi-session navigation system for ground vehicles. The system takes just a few hours from the beginning of the first manual scan to perform autonomous waypoint navigation.

The technological standards of photography have dramatically increased over the last few years. While cell phones used to not even have photo capture capabilities, nowadays, it is becoming more and more expected that modern smartphones can take pictures of a quality close to that of a dedicated camera. The computer vision community has recently focused on assessing image aesthetics for image cropping and learning systems for capturing cinematographic videos. In other words, researchers are asking themselves if robots can understand and capture aesthetically pleasing imagery. However, these systems often focus on composing specific objects of interest into the photo by using composition heuristics, like the rule of thirds, and do not translate directly to a system that can capture well-composed photographs in general.