@inproceedings{f55ab2a0c04f4908a77485c2faea9eb2,
title = "Evaluation of Long-term LiDAR Place Recognition",
abstract = "We compare a state-of-the-art deep image retrieval and a deep place recognition method for place recognition using LiDAR data. Place recognition aims to detect previously visited locations and thus provides an important tool for navigation, mapping, and localisation. Experimental comparisons are conducted using challenging outdoor and indoor datasets, Oxford Radar RobotCar and COLD, in the {"}long-term{"} setting where the test conditions differ substantially from the training and gallery data. Based on our results the image retrieval methods using LiDAR depth images can achieve accurate localization (the single best match recall 80%) within 5.00 m in urban outdoors. In office indoors the comparable accuracy is 50 cm but is more sensitive to changes in the environment.",
keywords = "Training, Meters, Location awareness, Laser radar, Image recognition, Image retrieval, Radar imaging",
author = "Jukka Peltom{\"a}ki and Farid Alijani and Jussi Puura and Heikki Huttunen and Esa Rahtu and K{\"a}m{\"a}r{\"a}inen, {J. -K.}",
note = "jufoid=70582; IEEE/RSJ International Conference on Intelligent Robots and Systems ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
doi = "10.1109/IROS51168.2021.9636320",
language = "English",
series = "Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems",
publisher = "IEEE",
pages = "4487--4492",
booktitle = "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
address = "United States",
}