A key component in the emerging localization and mapping paradigm is an appearance-based place recognition algorithm that detects when a place has been revisited. This algorithm can run in the background at a low frame rate and be used to signal a global geometric mapping algorithm when a loop is detected. An optimization technique can then be used to correct the map by 'closing the loop'. This allows an autonomous unmanned ground vehicle to improve localization and map accuracy and successfully navigate large environments. Image-based place recognition techniques lack robustness to sensor orientation and varying lighting conditions. Additionally, the quality of range estimates from monocular or stereo imagery can decrease the loop closure accuracy. Here, we present a lidar-based place recognition system that is robust to these challenges. This probabilistic framework learns a generative model of place appearance and determines whether a new observation comes from a new or previously seen place. Highly descriptive features called the Variable Dimensional Local Shape Descriptors are extracted from lidar range data to encode environment features. The range data processing has been implemented on a graphics processing unit to optimize performance. The system runs in real-time on a military research vehicle equipped with a highly accurate, 360 degree field of view lidar and can detect loops regardless of the sensor orientation. Promising experimental results are presented for both rural and urban scenes in large outdoor environments.
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