Mathias Buerki, Igor Gilitschenski, Elena Stumm, Roland Siegwart, and Juan Nieto International Conference on Intelligent Robots and Systems (IROS) 2016 We present an online landmark selection method for efficient and accurate visual localization under changing appearance conditions. The wide range of conditions encountered during long-term visual localization by e.g. fleets of autonomous vehicles offers the potential exploit redundancy and reduce data usage by selecting only those visual cues which are relevant at the given time. Therefore co-observability statistics guide landmark ranking and selection, significantly reducing the amount of information used for localization while maintaining or even improving accuracy. pdf   video...