Mapping and localization using surround view

Intelligent vehicles heavily rely on robust and accurate self-localization. Global navigation satellite systems (GNSS) are not reliable in urban environments due to multipath and shadowing effects. Vision-based localization offers a promising alternative. We present a high-precision six degrees of freedom self-localization method using multiple cameras covering the surrounding environment. First, a point feature map is created using images from a previous pass of the area to map. Thereafter, the map is used for high-precision localization in real-time. While localization, a rough prior estimate of the current pose is used to shrink the search space for feature matching by projecting mapped landmarks into current images. Then, stored observations of the projected landmarks are matched to actual observations and the egopose is estimated by back-projection error minimization. Thereby, our map structure provides mapped landmarks efficiently towards localization with multiple cameras. In real-world experiments we show that our approach provides reliable localization results while passing the mapped area in arbitrary orientation.