Multi-session lake-shore monitoring in visually challenging conditions

EasyChair Preprint 1424

Abstract

Long-term monitoring of natural environments raises significant challenges due to the strong perceptual aliasing in trees, bushes and shrubs. This paper reports on the multi-session localization and mapping of a small lake shore using an autonomous surface vessel equipped with a 2D lidar and a camera. Our publicly available dataset includes 130 autonomous surveys of the 1 km shoreline while recording lidar, GPS and image data. We build our globally consistent multi-session map using ICP at multiple scale. The end result is evaluated qualitatively by super-imposing all the lidar maps, and quantitatively by comparing images taken from the same pose at different times. The localization and mapping results, as well as the dataset of image pairs, are made available within our public dataset.

Keyphrases: Graph SLAM, Iterative Closest Point (ICP), Multi-session mapping, Shore monitoring

@booklet{EasyChair:1424,
  author    = {Cedric Pradalier and Stephanie Aravecchia and François Pomerleau},
  title     = {Multi-session lake-shore monitoring in visually challenging conditions},
  howpublished = {EasyChair Preprint 1424},
  year      = {EasyChair, 2019}}