Description and experimental results of a panoramic K‐band radar dedicated to perception in mobile robotics applications

Rouveure, R.; Faure, Patrice; Monod, M.O.

doi:10.1002/rob.21770

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…After these processing, the range, velocity and angle information of objects are obtained. In addition to these above issues, some other problems need to be considered, such as, the pre-processing of the raw ADC data, the constant false alarm rate (CFAR) 38,39 , and so on. However, these topics are out of scope of this paper and not discussed.…”

Section: Radar Detection Principlementioning

confidence: 99%

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired

Long

Wang

Cheng

et al. 2019

Review of Scientific Instruments

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It is very difficult for visually impaired people to perceive and avoid obstacles at a distance. To address this problem, the unified framework of multiple target detection, recognition and fusion is proposed based on the sensor fusion system comprised of a low-power MMW radar and an RGB-D sensor. In this paper, Mask R-CNN and SSD network are utilized to detect and recognize the objects from color images. The obstacles depth information is obtained from the depth images using the MeanShift algorithm. The position and velocity information of the multiple target are detected by the millimeter wave radar based on the principle of frequency modulated continuous wave. The data fusion based on the Particle Filter obtains more accurate state estimation and richer information by fusing the detection results from the color images, depth images and radar data compared with using only one sensor. The experiment results show that the data fusion enriches the detection results. Meanwhile, the effective detection range is expanded compared to using only the RGB-Depth sensor. Moreover, the data fusion results keep high accuracy and stability under diverse range and illumination conditions. As a wearable system, the sensor fusion system has the characteristics of versatility, portability and cost-effectiveness.

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Section: Radar Detection Principlementioning

confidence: 99%

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired

Long

Wang

Cheng

et al. 2019

Review of Scientific Instruments

View full text Add to dashboard Cite

show abstract

“…it is suitable for distinguishing between stationary and moving objects, and can be used to discard moving objects during the map-building process [62]. Some research on using Radar for SLAM can be found in [42,[62][63][64][65][66]. When compared to Lidar, lower price, lower power consumption, and less sensitivity to atmospheric conditions make it well suited for outdoor applications.…”

Section: Sensors and Fusion Methods For Slammentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

et al. 2023

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The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

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“…In addition to MIMO, the AoA can also be measured by a spinning directive antenna, for each moment the radar outputs an 1D power–range spectrum for the inspected line of sight, where the azimuth angle is the radar’s own azimuth angle relative to the fixed coordinate [ 115 ]. In [ 117 , 118 ], a designated spinning radar device called PELICAN was constructed and evaluated for mobile robotics applications.…”

Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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Description and experimental results of a panoramic K‐band radar dedicated to perception in mobile robotics applications

Abstract: Description and experimental results of a panoramic K-Band radar dedicated to perception in mobile robotics applications.

Cited by 8 publications

References 36 publications

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

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