All-Day Visual Place Recognition



Abstract

This paper introduces all-day dataset captured from KAIST campus for use in mobile robotics, autonomous driving, and recognition researches. Totally, we captured 42 km 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.


Publications and downloads

All-Day Visual Place Recognition: Benchmark Dataset and Baseline
Yukyung Choi, Namil Kim, Kibak Park, Soonmin Hwang, Jae‐Shin Yoon and In So Kweon 
In Proc. of Computer Vision and Pattern Recognition Workshop on Visual Place Recognition in Changing Environments (CVPRW-VPRICE) 2015.


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