Accurate localization by fusing images and GPS signals

Localization in 3D is an important problem with wide ranging applications from autonomous navigation in robotics to location specific services on mobile devices. GPS sensors are a commercially viable option for localization, and are ubiquitous in their use, especially in portable devices. With the proliferation of mobile cameras however, maturing localization algorithms based on computer vision are emerging as a viable alternative. Although both vision and GPS based localization algorithms have many limitations and inaccuracies, there are some interesting complimentarities in their success/failure scenarios that justify an investigation into their joint utilization. Such investigations are further justified considering that many of the modern wearable and mobile computing devices come with sensors for both GPS and vision. In this work, we investigate approaches to reinforce GPS localization with vision algorithms and vice versa. Specifically, we show how noisy GPS signals can be rectified by vision based localization of images captured in the vicinity. Alternatively, we also show how GPS readouts might be used to disambiguate images when they are visually similar looking but belong to different places. Finally, we empirically validate our solutions to show that fusing both these approaches can result in a more accurate and reliable localization of videos captured with a Contour action camera, over a 600 meter long path, over 10 different days.