All-Day Visual Place Recognition: Benchmark Dataset and Baselines

Abstract

This paper introduces all-day dataset captured from KAIST campus for use in mobile robotics, autonomous driving, and recognition researches. Totally, we captured 42km sequences at 15∼100Hz using multiple sensor modalities such as fully aligned visible and thermal devices, high resolution stereo visible cameras, and a high accuracy GPS/IMU inertial navigation system. Despites of a particular scenario, we provide the first aligned visible/thermal all-day dataset, including various illumination conditions: day, night, sunset, and sunrise. With this dataset, we introduce multi-spectral loop-detector as a baseline. We will open all calibrated and synchronized datasets, and hope to make a various state of the art computer vision and robotics algorithms.

Publication
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRw-VPRICE)
place recognition multispectral benchmark