A Hybrid SLAM and Object Recognition System for Pepper Robot

Abstract

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.

An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

“…Silva et al [22] studied Pepper's navigation with and without obstacle avoidance and illustrated that without obstacles, its success rate is higher. Ardon et al [23] utilized the combination of SLAM and object recognition systems to enhance Pepper's capability in indoor surroundings.…”

“…Although promising results were reported, the studies were conducted in a constrained space, suggesting additional research. Previous studies have described the improvement in Pepper's existing functionality, such as improving locomotion using control theory [31], 3D depth perception [32,33], and another using a combination of monocular perception and the in-built 3D sensor, navigation, and localization using the Robot operating system (ROS) [34], ORB SLAM [21], and an improved version of ORB SLAM 2 [23].…”

Active Simultaneous Localization and Mapping Method Based on Model Prediction

“…At the same time, the structure optimizes the perception goals for reliable sensing of key points for closing the loop by sampling the trajectory for maximum testing of points of interest. Loop closure is one of the most important contributions of SLAM [10]. Reliable loop closure allows one to change the card globally.…”