Visual SLAM-Based Localization and Navigation for Service Robots: The Pepper Case

Gómez, Cristopher; Mattamala, Matías; Resink, Tim; Ruiz‐del‐Solar, Javier

doi:10.1007/978-3-030-27544-0_3

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Alhmiedat et al [20] highlighted the problems with Pepper's navigation system. Gomez et al [21] mentioned that due to the small-range LIDARs and RGB, Pepper is not able to self-localize in larger surroundings. Silva et al [22] studied Pepper's navigation with and without obstacle avoidance and illustrated that without obstacles, its success rate is higher.…”

Section: Related Workmentioning

confidence: 99%

“…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].…”

Section: Map and Navigation Functionality Of Peppermentioning

confidence: 99%

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

Mishra,

Romero,

Pande

et al. 2023

Applied Sciences

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The application of social robots is increasing daily due to their various capabilities. In real settings, social robots have been successfully deployed in multiple domains, such as health, education, and the service industry. However, it is crucial to identify the strengths and limitations of a social robot before it can be employed in a real-life scenario. In this study, we explore and examine the capabilities of a humanoid robot, ‘Pepper’, which can be programmed to interact with humans. The present paper investigates five capabilities of Pepper: mapping and navigation, speech, hearing, object detection, and face detection. We attempt to study each of these capabilities in-depth with the help of experiments conducted in the laboratory. It has been identified that Pepper’s sound and speech recognition capabilities yielded satisfactory results, even with various accents. On the other hand, Pepper’s built-in SLAM navigation is unreliable, making it difficult to reach destinations accurately due to generated maps. Moreover, its object and face detection capabilities delivered inconsistent outcomes. This shows that Pepper has potential for improvement in its current capabilities. However, previous studies showed that with the integration of artificial intelligence techniques, a social robot’s capabilities can be enhanced significantly. In the future, we will focus on such integration in the Pepper robot, and the present study’s exploration will help to establish a baseline comprehension of the in-built artificial intelligence of Pepper. The findings of the present paper provide insights to researchers and practitioners planning to use the Pepper robot in their future work.

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Section: Related Workmentioning

confidence: 99%

Section: Map and Navigation Functionality Of Peppermentioning

confidence: 99%

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

Mishra,

Romero,

Pande

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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“…A suggestion to use more than the ranger sensors is made as future work objective in [ 18 ]. This suggestion is followed by the work in [ 19 ] which uses the RGB and depth sensors of Pepper to perform visual SLAM (specifically, an adapted version of the ORB-SLAM algorithm [ 20 ]), using the range sensors only for obstacle avoidance during navigation. However, in this case SLAM takes longer to complete and the approach has difficulty in estimating a correct localization in visually feature-poor environments (e.g., a large hall), given the narrow field of view of the RGB camera installed on Pepper.…”

Section: Related Workmentioning

confidence: 99%

The AMIRO Social Robotics Framework: Deployment and Evaluation on the Pepper Robot

Ghita

Gavril

Nan

et al. 2020

Sensors

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Recent studies in social robotics show that it can provide economic efficiency and growth in domains such as retail, entertainment, and active and assisted living (AAL). Recent work also highlights that users have the expectation of affordable social robotics platforms, providing focused and specific assistance in a robust manner. In this paper, we present the AMIRO social robotics framework, designed in a modular and robust way for assistive care scenarios. The framework includes robotic services for navigation, person detection and recognition, multi-lingual natural language interaction and dialogue management, as well as activity recognition and general behavior composition. We present AMIRO platform independent implementation based on a Robot Operating System (ROS). We focus on quantitative evaluations of each functionality module, providing discussions on their performance in different settings and the possible improvements. We showcase the deployment of the AMIRO framework on a popular social robotics platform—the Pepper robot—and present the experience of developing a complex user interaction scenario, employing all available functionality modules within AMIRO.

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