A Miniature Vision-Based Localization System for Indoor Blimps

TL;DR

This paper presents a visual localization system enabling indoor blimps to autonomously determine their position using a monocular camera and WiFi, facilitating applications like surveillance and indoor mapping.

Contribution

It introduces a novel visual localization approach for indoor blimps using Structure from Motion and pose estimation with minimal onboard sensors.

Findings

Successful environment reconstruction with Structure from Motion

Effective camera pose estimation from visual features

Potential for autonomous indoor blimp navigation

Abstract

With increasing attention paid to blimp research, I hope to build an indoor blimp to interact with humans. To begin with, I propose developing a visual localization system to enable blimps to localize themselves in an indoor environment autonomously. This system initially reconstructs an indoor environment by employing Structure from Motion with Superpoint visual features. Next, with the previously built sparse point cloud map, the system generates camera poses by continuously employing pose estimation on matched visual features observed from the map. In this project, the blimp only serves as a reference mobile platform that constrains the weight of the perception system. The perception system contains one monocular camera and a WiFi adaptor to capture and transmit visual data to a ground PC station where the algorithms will be executed. The success of this project will transform remote-controlled indoor blimps into autonomous indoor blimps, which can be utilized for applications such as surveillance, advertisement, and indoor mapping.