Modeling structure and aerosol concentration with fused radar and LiDAR data in environments with changing visibility

Fritsche, Paul; Wagner, Bernd

doi:10.1109/iros.2017.8206093

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…These periods are represented as exponential changes. To calculate the response of the sensor to the VOC (to eliminate the change in resistance due to heater temperature) and to remove baseline drift, the 'OFF' period (2) is subtracted from the 'ON' period (1). An FFT of the data is then calculated, using an algorithm optimized for use on microcontroller in real-time.…”

Section: Methodsmentioning

confidence: 99%

“…Mobile robots are ideal tools for exploring hazardous areas without placing the lives of emergency workers at risk. In a disaster zone, such as a burning building, visibility can be poor, and the robot requires sensors other than machine vision to navigate, such as LIDAR and RADAR [1]. In this work we have developed high-bandwidth (HBW) gas sensors to detect plumes of VOCs.…”

Section: Introductionmentioning

confidence: 99%

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Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

et al. 2018

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A new signal processing technique has been developed for resistive metal oxide (MOX) gas sensors to enable high-bandwidth measurements and enhanced selectivity at PPM levels (<50 PPM VOCs). An embedded micro-heater is thermally pulsed from 225 to 350 °C, which enables the chemical reactions in the sensor film (e.g., SnO2, WO3, NiO) to be extracted using a fast Fourier transform. Signal processing is performed in real-time using a low-cost microcontroller integrated into a sensor module. The approach enables the remove of baseline drift and is resilient to environmental temperature changes. Bench-top experimental results are presented for 50 to 200 ppm of ethanol and CO, which demonstrate our sensor system can be used within a mobile robot.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

et al. 2018

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“…3) Sensor fusion for SLAM: As LiDAR cannot see through occulusions such as smoke, fog or dust, [15] proposes to fuse LiDAR data with Radar data to handle the SLAM problem in the environments with low visibility. The fusion takes place on scan level and map level to maximize the map quality considering the visibility situation.…”

Section: Uwb-only Slam Scan Matchingmentioning

confidence: 99%

UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Song

Guan

Tay

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We equip an ultra-wideband (UWB) node and a 2D LiDAR sensor a.k.a. 2D laser rangefinder on a mobile robot, and place UWB beacon nodes at unknown locations in an unknown environment. All UWB nodes can do ranging with each other thus forming a cooperative sensor networks. We propose to fuse the peer-to-peer ranges measured between UWB nodes and laser scanning information, i.e. range measured between robot and nearby objects/obstacles, for simultaneous localization of the robot, all UWB beacons and LiDAR mapping. The fusion is inspired by two facts: 1) LiDAR may improve UWB-only localization accuracy as it gives more precise and comprehensive picture of the surrounding environment; 2) on the other hand, UWB ranging measurements may remove the error accumulated in the LiDAR-based SLAM algorithm. Our experiments demonstrate that UWB/LiDAR fusion enables driftfree SLAM in real-time based on ranging measurements only.

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“…In this study, the antenna was mechanically rotated 360 degrees to detect multiple targets at the same time. Moreover, the authors in [ 6 , 7 ] presented the mapping results in indoor smoky situations. In both studies, a radar system developed by Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR) that mechanically rotates the antenna through a motor was applied.…”

Section: Introductionmentioning

confidence: 99%

Dual-Mode Radar Sensor for Indoor Environment Mapping

Lee

Kwon

Kim³

et al. 2021

Sensors

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In this paper, we introduce mapping results in an indoor environment based on our own developed dual-mode radar sensor. Our radar system uses a frequency-modulated continuous wave (FMCW) with a center frequency of 62 GHz and a multiple-input multiple-output antenna system. In addition, the FMCW radar sensor we designed is capable of dual-mode detection, which alternately transmits two waveforms using different bandwidths within one frame. The first waveform is for long-range detection, and the second waveform is for short-range detection. This radar system is mounted on a small robot that moves in indoor environments such as rooms or hallways, and the radar and the robot send and receive necessary information to each other. The radar estimates the distance, velocity, and angle information of targets around the radar-equipped robot. Then, the radar receives information about the robot’s motion from the robot, such as its speed and rotation angle. Finally, by combining the motion information and the detection results, the radar-equipped robot maps the indoor environment while finding its own position. Compared to the actual map data, the radar-based mapping is effectively achieved through the radar system we developed.

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Modeling structure and aerosol concentration with fused radar and LiDAR data in environments with changing visibility

Cited by 14 publications

References 14 publications

Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Dual-Mode Radar Sensor for Indoor Environment Mapping

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