Loop-closure detection by LiDAR scan re-identification
Peltomäki, Jukka; Ni, Xingyang; Puura, Jussi; Kämäräinen, Joni Kristian; Huttunen, Heikki (2021)
Peltomäki, Jukka
Ni, Xingyang
Puura, Jussi
Kämäräinen, Joni Kristian
Huttunen, Heikki
IEEE
2021
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202211218483
https://urn.fi/URN:NBN:fi:tuni-202211218483
Kuvaus
Peer reviewed
Tiivistelmä
In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 0.94.
Kokoelmat
- TUNICRIS-julkaisut [16977]