  Accurate and safety-quantifiable localization is of great significance for
safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and
unmanned aerial vehicles (UAV). The visual odometry-based method can provide
accurate positioning in a short period but is subjected to drift over time.
Moreover, the quantification of the safety of the localization solution (the
error is bounded by a certain value) is still a challenge. To fill the gaps,
this paper proposes a safety-quantifiable line feature-based visual
localization method with a prior map. The visual-inertial odometry provides a
high-frequency local pose estimation which serves as the initial guess for the
visual localization. By obtaining a visual line feature pair association, a
foot point-based constraint is proposed to construct the cost function between
the 2D lines extracted from the real-time image and the 3D lines extracted from
the high-precision prior 3D point cloud map. Moreover, a global navigation
satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM)
inspired method is employed to quantify the safety of the derived localization
solution. Among that, an outlier rejection (also well-known as fault detection
and exclusion) strategy is employed via the weighted sum of squares residual
with a Chi-squared probability distribution. A protection level (PL) scheme
considering multiple outliers is derived and utilized to quantify the potential
error bound of the localization solution in both position and rotation domains.
The effectiveness of the proposed safety-quantifiable localization system is
verified using the datasets collected in the UAV indoor and UGV outdoor
environments.
