Investigation on Robustness of Vehicle Localization Using Cameras and LiDAR

Albrecht, Christian Rudolf; Behre, Jenny; Herrmann, Eva; Jurgens, Stefan; Stilla, Uwe

doi:10.3390/vehicles4020027

Cited by 4 publications

(3 citation statements)

References 40 publications

(60 reference statements)

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“…The robustness of vehicle localization using cameras and LiDAR under various weather and illumination circumstances was examined in the study, "Investigation on Robustness of Vehicle Localization Using Cameras and LiDAR". The accuracy and resilience of various algorithms' localization abilities were measured by the authors under various conditions, such as heavy snowfall, rain, and fog, which emphasized the significance of the aforementioned point [87].…”

Section: Robustnessmentioning

confidence: 96%

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Rathnayake

Maduranga

Tilwari

et al. 2023

Eng

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The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

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Section: Robustnessmentioning

confidence: 96%

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Rathnayake

Maduranga

Tilwari

et al. 2023

Eng

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“…They train a model in a metric learning setting to extract visual features from ground and aerial images. Albrecht et al 10 investigate the robustness of vehicle localization using cameras and LiDAR sensors. They propose a novel robustness score for test drives to identify system limitations and possible improvements.…”

Section: Related Workmentioning

confidence: 99%

Extraction and matching of 3D features for LiDAR-based self-localization in an urban environment

Hammer

Hebel²,

Arens³

2022

Electro-Optical Remote Sensing XVI

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Geolocation of vehicles, objects or people is commonly done using global navigation satellite system (GNSS) receivers. Such a receiver for GNSS-based positioning is either built into the vehicle, or separate handheld devices like a smartphone or similar are used. Self-localization in this way is simple and accurate up to a few meters.Environments where no GNSS service is available require other strategies for self-localization. Especially in the military domain, it is necessary to be prepared for such GNSS-denied scenarios. Awareness of the own position in relation to other units is crucial in military operations, especially where joint operations have to be coordinated geographically and temporally. However, even if a common map-like representation of the terrain is available, precise self-localization relative to this map is not necessarily easy.In this paper, we propose an approach for LiDAR-based localization of a vehicle-based sensor platform in an urban environment. Our approach is to use 360°scanning LiDAR sensors to generate short-duration point clouds of the local environment. In these point clouds, we detect pole-like 3D features such as traffic sign poles, lampposts or tree trunks. The relative distance and orientation of these features to each other is rather unique, and the matrix of these individual distances and orientations can be used to determine the position of the sensor relative to a current map. This map can either be created in advance for the entire area, or a cooperative preceding vehicle with an equivalent sensor setup can generate it. By matching the found LiDARbased 3D features with those of the map, not only the position of the sensor platform but also its orientation can be determined. We provide first experimental results of the proposed method, which were achieved with measurements by Fraunhofer IOSB's sensor-equipped vehicle MODISSA.

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“…Research on sensor data processing methods has mainly focused on improving perception performance by studying methods for processing specific sensor data or by combining data from different sensor sets [20][21][22][23][24]. In addition, much of the research on autonomous driving software has aimed at improving the performance of autonomous driving software platforms and architectures [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Design of Sensor Data Processing Software for the ISO 23150 Standard: Application to Autonomous Vehicle Software

Han,

Park,

Kim

et al. 2023

Electronics

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The ISO 23150 standard defines the logical interface between sensors and fusion units. To apply this standard to actual vehicles, software is required to convert sensor data into ISO 23150-compliant sensor data. In this study, we developed sensor data processing software to provide ISO 23150-compliant sensor data to autonomous vehicle software. The main contributions of this study are as follows: First, the safety of the software is considered, and its structure and error detection method are designed to minimize the impact of errors. Second, the software structure is in accordance with the ISO 23150 standard, and a framework structure is designed with convenience in mind. Third, we considered its compatibility with adaptive AUTOSAR by designing a data delivery service using SOME/IP communication. We evaluated the security and data delivery delay of the software on a controller used in an actual vehicle and noted high security and real-time performance. The evaluation results demonstrated the feasibility of this method for real-world vehicles. Our study can serve as a basis for advancing autonomous driving technology in the context of ensuring software safety.

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Investigation on Robustness of Vehicle Localization Using Cameras and LiDAR

Cited by 4 publications

References 40 publications

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Extraction and matching of 3D features for LiDAR-based self-localization in an urban environment

Design of Sensor Data Processing Software for the ISO 23150 Standard: Application to Autonomous Vehicle Software

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