HD Map Change Detection with a Boosted Particle Filter
David Pannen, Martin Liebner, Wolfram Burgard
Abstract
In this paper, we present a change detection algorithm that can run in real time as part of a backend-based stream processing pipeline. It can process the floating car data collected by series-production vehicles to detect changes in an automotive high definition digital (HD) map used for automated driving. The algorithm uses a particle filter approach with odometry, GNSS and landmark readings to localize the vehicle within the digital map. While all particles together represent the probability distribution for the vehicle's position at a given time, each individual particle also serves as a hypothesis about the vehicle's position. This is used to compute various metrics for how well the current sensor readings match the world model encoded in the HD map. The different metrics are evaluated by a number of weak classifiers that are used as input for a trained Adaboost classifier. The achievable detection rate of a single vehicle is then compared to that of a simple crowd-based approach, where each vehicle votes on whether or not the current section of the road has changed.
BibTeX
@inproceedings{icra2019_hdmapchangedetec,
title = {HD Map Change Detection with a Boosted Particle Filter},
author = {David Pannen and Martin Liebner and Wolfram Burgard},
booktitle = {ICRA 2019},
year = {2019}
}