RGB-D, Laser and Thermal Sensor Fusion for People following in a Mobile Robot

Susperregi, Loreto; Martínez-Otzeta, José María; Ansuategui, Ander; Ibarguren, Aitor; Sierra, Basilio

doi:10.5772/56123

Cited by 37 publications

(24 citation statements)

References 16 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in sensing and estimation Susperregi et al, 2013;Luo et al, 2009). A summary of the key design issues based on different choices of sensors for personfollowing UGVs is presented in Table 1.…”

Section: Choice Of Sensorsmentioning

confidence: 99%

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, some of the prominent methods are qualitatively compared, corresponding practicalities are illustrated, and their feasibility is analyzed for various usecases. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.

show abstract

Section: Choice Of Sensorsmentioning

confidence: 99%

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…In addition, calibration between depth and color sensors by a maximum likelihood solution by using checker board is performed in [16,17]. In [18], a multiple sensor fusion system, which combines RGB-Dvision, lasers and a thermal sensor, in order to detect people, in a mobile robot.…”

Section: Related Workmentioning

confidence: 99%

3D Multi-Spectrum Sensor System with Face Recognition

Kim

et al. 2013

Sensors

View full text Add to dashboard Cite

This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained.

show abstract

“…However, it is difficult to obtain the desired positioning accuracy because of the inherent systematic errors and the accumulated measurement error [2]. Therefore, recently developed technologies such as LASER point cloud and depth sensor have been applied to SLAM in recent years [3][4][5][6]. Point cloud registration is a technology to calculate the displacement of two point clouds.…”

Section: Introductionmentioning

confidence: 99%

The Pose Estimation of Mobile Robot Based on Improved Point Cloud Registration

Miao

Liu

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Due to GPS restrictions, an inertial sensor is usually used to estimate the location of indoor mobile robots. However, it is difficult to achieve high-accuracy localization and control by inertial sensors alone. In this paper, a new method is proposed to estimate an indoor mobile robot pose with six degrees of freedom based on an improved 3D-Normal Distributions Transform algorithm (3D-NDT). First, point cloud data are captured by a Kinect sensor and segmented according to the distance to the robot. After the segmentation, the input point cloud data are processed by the Approximate Voxel Grid Filter algorithm in different sized voxel grids. Second, the initial registration and precise registration are performed respectively according to the distance to the sensor. The most distant point cloud data use the 3D-Normal Distributions Transform algorithm (3D-NDT) with large-sized voxel grids for initial registration, based on the transformation matrix from the odometry method. The closest point cloud data use the 3D-NDT algorithm with small-sized voxel grids for precise registration. After the registrations above, a final transformation matrix is obtained and coordinated. Based on this transformation matrix, the pose estimation problem of the indoor mobile robot is solved. Test results show that this method can obtain accurate robot pose estimation and has better robustness.

show abstract

RGB-D, Laser and Thermal Sensor Fusion for People following in a Mobile Robot

Cited by 37 publications

References 16 publications

Person-following by autonomous robots: A categorical overview

Person-following by autonomous robots: A categorical overview

3D Multi-Spectrum Sensor System with Face Recognition

The Pose Estimation of Mobile Robot Based on Improved Point Cloud Registration

Contact Info

Product

Resources

About