The improvement of odometry systems in collective robotics remains an important challenge for several applications. Social odometry is an online social dynamics which confers the robots the possibility to learn from the others. Robots neither share any movement constraint nor access to centralized information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance traveled. Social odometry guides a robot to its goal by imitating estimated locations, confidence levels and actual locations of its neighbors. This simple online social form of odometry is shown to produce a self-organized collective pattern which allows a group of robots to both increase the quality of individuals' estimates and efficiently improve their collective performance.