Multi-Sensor Appearance-Based Place Recognition
This paper describes a multi-sensor appearance-based place recognition system suitable for robotic mapping.
Unlike systems that extract features from visual imagery only, here we apply the well known Bag-of-Words
approach to features extracted from both visual and range sensors. By applying this technique to both sensor
streams simultaneously we can overcome the deficiencies of each individual sensor. We show that lidar-based
place recognition using a generative model learnt from Variable Dimensional Local Shape Descriptors can be used to
perform place recognition regardless of lighting conditions or large changes in orientation, including traversing
loops backward. Likewise, we are still able to exploit the feature rich place recognition that visual systems
provide. Using a pose verification system we are able to effectively discard false positive loop detections. We
present experimental results that highlight the strength of our approach and investigate alternative techniques
for combining the results from the individual sensor streams. The multi-sensor approach enables the two sensors
to complement each other well in large urban and rural environments under variable lighting conditions.