Kidnapping occurs when a robot is unaware that it has not correctly ascertained its position. As a result, the global map may be severely deformed and the robot may be unable to perform its function. This paper presents a metric-based technique for real-time kidnap detection that utilises a set of binary classifiers to identify all kidnapping events during the autonomous operation of a mobile robot. In contrast, existing techniques either solve specific cases of kidnapping, such as elevator motion, without addressing the general case or remove dependence on local pose estimation entirely, an inefficient and computationally expensive approach. Four metrics were evaluated and the optimal thresholds for the most suitable metrics were determined, resulting in a combined detector that has a negligible probability of failing to identify kidnapping events and a low false positive rate for both indoor and outdoor environments. While this paper uses metrics specific to 3D point clouds, the approach can be generalised to other forms of data, including visual, providing that two independent ways of estimating pose are available. © 2013 IEEE.