Bots2ReC: Radar Localization in Low Visibility Indoor Environments

Mandischer, Nils; Eddine, Sami Charaf; Huesing, Mathias; Corves, Burkhard

doi:10.1109/ssrr.2019.8848981

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…Further, in this scenario it is more important to establish a robust track than to initialize tracks faster (which would be the more general case). In prior work (Mandischer et al, 2019), we have indicated that radar has no impairments by smoke or dust (see also Fang et al, 2022;Zhang et al, 2022), therefore, we assume that the results are similar to the firefighting use-case. However, future tests will need to support this assumption.…”

Section: Discussionmentioning

confidence: 86%

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Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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Leg tracking is an established field in mobile robotics and machine vision in general.These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail.Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

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Section: Discussionmentioning

confidence: 86%

“…In prior work (Mandischer et al, 2019), we observed that the high level of noise in radar scans deployed in indoor environments may be lowered by applying an adaptive threshold. Therefore, the radar scan is initially thresholded using the approach proposed by Otsu (1979).…”

Section: Radar Prefilteringmentioning

confidence: 99%

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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“…Moreover, a multi-modality framework has proposed to cope with difficulties in percepting the environment around the vehicle at low visibility. In such frameworks, the robot's pose was estimated by using a multi-sensory data fusion technique, i.e., thermal imager with IMU [14], [201], eventbased camera with IMU [196], [197], [221], thermal imager and LiDAR measurements [16], Radar and LiDAR [214], and more than two sensory data [199], [200], [219].…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…Recently, two localization techniques in low visibility environments were proposed by Mandischer et al [214], a novel radar-based SLAM and another radar-based localization strategy employing laser maps. These approaches are evaluated in indoor environments with heavy dust formulation to emulate vision scenarios of the grinding process.…”

Section: Localization Techniques In Low-visibility Environmentsmentioning

confidence: 99%

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

2021

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Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.

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“…The system delivers a small change in distance (6% with outliers) during a thick smoke, as well as during a light smoke with high temperature test. Mandischer et al 39 introduce a strategy to steer in low visibility environments with radar and an efficient radar filtering process. The process consists of three steps and enables a consistent noise reduction.…”

Section: Radarmentioning

confidence: 99%

A sensor review for human detection with robotic systems in regular and smoky environments

Gelfert

2023

International Journal of Advanced Robotic Systems

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Search and rescue robots gained a significant attention in the past, as they assist firefighters during their rescue missions. The opportunity to move autonomously or remotely controlled with intelligent sensor technology, to detect victims in unknown fire smoke environments, introduces a growing technology in fire engineering. Since sensor systems are a component of mobile robots, there is a demand for intelligent robot vision, especially for human detection in fire smoke environments. In this article, an overview of sensor technologies and their algorithms for human detection in regular and smoky environments is presented. These sensor technologies are categorized into single sensor and multi-sensor systems. Novel sensor approaches are led by artificial intelligence, 3D mapping and multi-sensor fusion. The article provides a contribution for future research directions in algorithms and applications and supports decision-makers in fire engineering to get knowledge in trends, novel applications and challenges in this field of research.

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Bots2ReC: Radar Localization in Low Visibility Indoor Environments

Cited by 5 publications

References 11 publications

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

A sensor review for human detection with robotic systems in regular and smoky environments

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