4 research outputs found
A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration
We present a family of methods for 2D–3D registration spanning both deterministic and non-deterministic branch-and-bound approaches. Critically, the methods exhibit invariance to the underlying scene primitives, enabling e.g. points and lines to be treated on an equivalent basis, potentially enabling a broader range of problems to be tackled while maximising available scene information, all scene primitives being simultaneously considered. Being a branch-and-bound based approach, the method furthermore enjoys intrinsic guarantees of global optimality; while branch-and-bound approaches have been employed in a number of computer vision contexts, the proposed method represents the first time that this strategy has been applied to the 2D–3D correspondence-free registration problem from points and lines. Within the proposed procedure, deterministic and probabilistic procedures serve to speed up the nested branch-and-bound search while maintaining optimality. Experimental evaluation with synthetic and real data indicates that the proposed approach significantly increases both accuracy and robustness compared to the state of the art
A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration
We present a family of methods for 2D–3D registration spanning both deterministic and non-deterministic branch-and-bound approaches. Critically, the methods exhibit invariance to the underlying scene primitives, enabling e.g. points and lines to be treated on an equivalent basis, potentially enabling a broader range of problems to be tackled while maximising available scene information, all scene primitives being simultaneously considered. Being a branch-and-bound based approach, the method furthermore enjoys intrinsic guarantees of global optimality; while branch-and-bound approaches have been employed in a number of computer vision contexts, the proposed method represents the first time that this strategy has been applied to the 2D–3D correspondence-free registration problem from points and lines. Within the proposed procedure, deterministic and probabilistic procedures serve to speed up the nested branch-and-bound search while maintaining optimality. Experimental evaluation with synthetic and real data indicates that the proposed approach significantly increases both accuracy and robustness compared to the state of the art
Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map
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