TY - GEN
T1 - Long-term Visual Place Recognition
AU - Alijani, Farid
AU - Peltomäki, Jukka
AU - Puura, Jussi
AU - Huttunen, Heikki
AU - Kämäräinen, Joni-Kristian
AU - Rahtu, Esa
N1 - Publisher Copyright:
© 2022 IEEE.
jufoid=58099
PY - 2022
Y1 - 2022
N2 - In this work, we study the long-term performance of visual place recognition in urban outdoor environment. A long-term benchmark is constructed from the Oxford RobotCar dataset. It contains sequences of the same route traversed over a period of approx. 500 days. We carefully selected three gallery sequences, one training sequence and 15 query sequences that cover different seasons, times of day and weather. The RobotCar sequences from the first half year have several problems, for example, only partial routes and inaccurate location data. We circumvent these problems by reversing the time. In the benchmark dataset the gallery and training images are the latest and the query sequences go gradually back in time. Our experiments provide the following findings. 1) the selected gallery sequence has strong impact on performance, and 2) additional training sequences help to mitigate differences between the gallery sequences. In addition, results indicate that 3) there is a long-term trend of performance degradation over time. The degradation can be quantified as about 6 percentage points per 100 days and, therefore, the initial performance of 40% eventually drops below 20% at the end.
AB - In this work, we study the long-term performance of visual place recognition in urban outdoor environment. A long-term benchmark is constructed from the Oxford RobotCar dataset. It contains sequences of the same route traversed over a period of approx. 500 days. We carefully selected three gallery sequences, one training sequence and 15 query sequences that cover different seasons, times of day and weather. The RobotCar sequences from the first half year have several problems, for example, only partial routes and inaccurate location data. We circumvent these problems by reversing the time. In the benchmark dataset the gallery and training images are the latest and the query sequences go gradually back in time. Our experiments provide the following findings. 1) the selected gallery sequence has strong impact on performance, and 2) additional training sequences help to mitigate differences between the gallery sequences. In addition, results indicate that 3) there is a long-term trend of performance degradation over time. The degradation can be quantified as about 6 percentage points per 100 days and, therefore, the initial performance of 40% eventually drops below 20% at the end.
U2 - 10.1109/ICPR56361.2022.9956392
DO - 10.1109/ICPR56361.2022.9956392
M3 - Conference contribution
AN - SCOPUS:85143640006
T3 - International Conference on Pattern Recognition
SP - 3422
EP - 3428
BT - 2022 26th International Conference on Pattern Recognition, ICPR 2022
PB - IEEE
T2 - International Conference on Pattern Recognition
Y2 - 21 August 2022 through 25 August 2022
ER -