  Autonomous service mobile robots need to consistently, accurately, and
robustly localize in human environments despite changes to such environments
over time. Episodic non-Markov Localization addresses the challenge of
localization in such changing environments by classifying observations as
arising from Long-Term, Short-Term, or Dynamic Features. However, in order to
do so, EnML relies on an estimate of the Long-Term Vector Map (LTVM) that does
not change over time. In this paper, we introduce a recursive algorithm to
build and update the LTVM over time by reasoning about visibility constraints
of objects observed over multiple robot deployments. We use a signed distance
function (SDF) to filter out observations of short-term and dynamic features
from multiple deployments of the robot. The remaining long-term observations
are used to build a vector map by robust local linear regression. The
uncertainty in the resulting LTVM is computed via Monte Carlo resampling the
observations arising from long-term features. By combining occupancy-grid based
SDF filtering of observations with continuous space regression of the filtered
observations, our proposed approach builds, updates, and amends LTVMs over
time, reasoning about all observations from all robot deployments in an
environment. We present experimental results demonstrating the accuracy,
robustness, and compact nature of the extracted LTVMs from several long-term
robot datasets.
