A Hybrid SLAM and Object Recognition System for Pepper Robot
This work provides a practical solution for enhancing autonomy in humanoid robots like Pepper, though it is incremental as it integrates existing methods.
The authors developed a hybrid system combining SLAM and object recognition for the Pepper robot, demonstrating its applicability in real-world indoor scenarios.
Humanoid robots are playing increasingly important roles in real-life tasks especially when it comes to indoor applications. Providing robust solutions for the tasks such as indoor environment mapping, self-localisation and object recognition are essential to make the robots to be more autonomous, hence, more human-like. The well-known Aldebaran service robot Pepper is a suitable candidate for achieving these goals. In this paper, a hybrid system combining Simultaneous Localisation and Mapping (SLAM) algorithm with object recognition is developed and tested with Pepper robot in real-world conditions for the first time. The ORB SLAM 2 algorithm was taken as a seminal work in our research. Then, an object recognition technique based on Scale-Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC) was combined with SLAM to recognise and localise objects in the mapped indoor environment. The results of our experiments showed the system's applicability for the Pepper robot in real-world scenarios. Moreover, we made our source code available for the community at https://github.com/PaolaArdon/Salt-Pepper.