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Humanoid robots are playing increasingly important roles in real-life tasksespecially when it comes to indoor applications. Providing robust solutions forthe tasks such as indoor environment mapping, self-localisation and objectrecognition are essential to make the robots to be more autonomous, hence, morehuman-like. The well-known Aldebaran service robot Pepper is a suitablecandidate for achieving these goals. In this paper, a hybrid system combiningSimultaneous Localisation and Mapping (SLAM) algorithm with object recognitionis developed and tested with Pepper robot in real-world conditions for thefirst time. The ORB SLAM 2 algorithm was taken as a seminal work in ourresearch. Then, an object recognition technique based on Scale-InvariantFeature Transform (SIFT) and Random Sample Consensus (RANSAC) was combined withSLAM to recognise and localise objects in the mapped indoor environment. Theresults of our experiments showed the system's applicability for the Pepperrobot in real-world scenarios. Moreover, we made our source code available forthe community at https://github.com/PaolaArdon/Salt-Pepper.

A Hybrid SLAM and Object Recognition System for Pepper Robot