Fusing LiDAR and Radar Data to Perform SLAM in Harsh Environments

Fritsche, Paul; Kueppers, Simon; Briese, Gunnar; Wagner, Bernd

doi:10.1007/978-3-319-55011-4_9

Cited by 15 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third work 59 focuses in optimizing the quality of the map. In addition, the authors 59 reach to model the concentration of aerosols with combined radar and LIDAR data. The authors 59 use a finite model.…”

Section: Multi-sensor Approachesmentioning

confidence: 99%

“…Compared to a single sensor approach, this approach allows robustness to environmental unpredictability. Further works [57][58][59] examine the fusion of radar and LIDAR data in more detail. The first work 57 describes a fusion strategy to reduce the negative influence on LIDAR in thick smoke by introducing a high bandwidth radar (2D).…”

Section: Radar and Lidarmentioning

confidence: 99%

“…The second work 58 introduces two ways for merging LIDAR and radar data to achieve simultaneous localization and mapping in an environment with low viewability. The third work 59 focuses in optimizing the quality of the map. In addition, the authors 59 reach to model the concentration of aerosols with combined radar and LIDAR data.…”

Section: Radar and Lidarmentioning

confidence: 99%

See 2 more Smart Citations

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

Gelfert

2023

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Multi-sensor Approachesmentioning

confidence: 99%

Section: Radar and Lidarmentioning

confidence: 99%

See 1 more Smart Citation

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

Gelfert

2023

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…The robotic platform used is a Taurob tracker ( Biegl et al, 2014 ) (shown in Figure 6B ), which is built to help CBRN (Chemical, Biological, Radiological and Nuclear) first responders, EOD (explosive ordnance disposal) teams, fire-fighters and search & rescue teams to gain first-hand information for emergency response. The Taurob tracker can be equipped with a novel 3D radar camera, a 3D laser scanning sensor and a thermal camera that allow for robust mapping and navigation under low visibility conditions ( Fritsche et al, 2018 ). The unknown and uncontrolled sensing conditions assumed in this work are possibly shared with the target environments of Taurob robots.…”

Section: Data Set and Experimental Set-upmentioning

confidence: 99%

Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications

2022

View full text Add to dashboard Cite

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.

show abstract

“…Radar and camera together reach close to the LiDAR tracking ability and they concluded that this mixture stands a good chance in adverse weather conditions. Fritsche et al [144] used a 2D high bandwidth scanner, the mechanical pivoting radar (MPR) [145], to fuse with LiDAR data to achieve SLAM in a low visibility fog environment. The MPR only has a 15m measurement range but the one ability of penetrating fog is more than enough to prove itself useful in landmarks searching and make up for what the LiDAR is missing.…”

Section: Radar Dominantmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

show abstract

Fusing LiDAR and Radar Data to Perform SLAM in Harsh Environments

Cited by 15 publications

References 11 publications

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

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

Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Contact Info

Product

Resources

About