  Visual navigation and three-dimensional (3D) scene reconstruction are
essential for robotics to interact with the surrounding environment.
Large-scale scenes and critical camera motions are great challenges facing the
research community to achieve this goal. We raised a pose-only imaging geometry
framework and algorithms that can help solve these challenges. The
representation is a linear function of camera global translations, which allows
for efficient and robust camera motion estimation. As a result, the spatial
feature coordinates can be analytically reconstructed and do not require
nonlinear optimization. Experiments demonstrate that the computational
efficiency of recovering the scene and associated camera poses is significantly
improved by 2-4 orders of magnitude. This solution might be promising to unlock
real-time 3D visual computing in many forefront applications.
