Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Schlager, Birgit; Goelles, Thomas; Muckenhuber, Stefan; Watzenig, Daniel

doi:10.1109/ojits.2022.3214094

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…LiDAR performance degradation has also been studied for outer covers to prevent the addition of mechanical damage and surface contamination near the LiDAR aperture [18,19]. These studies showed that scratches on the cover affect the detection accuracy for a known position of the target.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives

Pao,

Howorth,

et al. 2024

Sensors

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With the emergence of autonomous functions in road vehicles, there has been increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor, detecting the positions of obstacles by representing them as clusters of points in three-dimensional space. LiDAR performance degrades significantly when a vehicle is driving in the rain as raindrops adhere to the outer surface of the sensor assembly. Performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradation is highly dependent on the interface material properties. This subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of a LiDAR assembly coated with four classes of material—hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to the different droplets of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis. The results are validated with physical optical tests, using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations of LiDAR performance degradation in rain are presented from both material and optical perspectives. These can aid component selection and the development of signal-enhancing strategies for the integration of LiDARs into vehicle designs to minimize the impact of rain.

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Section: Introductionmentioning

confidence: 99%

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives

Pao,

Howorth,

et al. 2024

Sensors

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“…However, newly emerging technologies, such as MEMS-based mirrors, optical phased arrays, vertical-cavity surface-emitting laser, and single photon avalanche diodes [21], [60], [63] will most likely enable commercial lidar sensors at costs low enough for implementation in standard vehicles in the next few years. While lidar is considered a robust sensor modality under adverse weather conditions, dew, dirt, or foam can lead to unwanted reflections at the sensor cover, which degrades sensor performance up to complete sensor blindness [54].…”

Section: Introductionmentioning

confidence: 99%

Occlusion Model—A Geometric Sensor Modeling Approach for Virtual Testing of ADAS/AD Functions

Genser

Muckenhuber

Gaisberger

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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New advanced driver assistance system/automated driving (ADAS/AD) functions have the potential to significantly enhance the safety of vehicle passengers and road users, while also enabling new transportation applications and potentially reducing CO2 emissions. To achieve the next level of driving automation, i.e., SAE Level-3, physical test drives need to be supplemented by simulations in virtual test environments. A major challenge for today's virtual test environments is to provide a realistic representation of the vehicle's perception system (camera, lidar, radar). Therefore, new and improved sensor models are required to perform representative virtual tests that can supplement physical test drives. In this article, we present a computationally efficient, mathematically complete, and geometrically exact generic sensor modeling approach that solves the FOV (field of view) and occlusion task. We also discuss potential extensions, such as bounding-box cropping and sensor-specific, weather-dependent FOV-reduction approaches for camera, lidar, and radar. The performance of the new modeling approach is demonstrated using camera measurements from a test campaign conducted in Hungary in 2020 plus three artificial scenarios (a multi-target scenario with an adjacent truck occluding other road users and two traffic jam situations in which the ego vehicle is either a car or a truck). These scenarios are benchmarked against existing sensor modeling approaches that only exclude objects that are outside the sensor's maximum detection range or angle. The modeling approach presented can be used as is or provide the basis for a more complex sensor model, as it reduces the number of potentially detectable targets and therefore improves the performance of subsequent simulation steps.

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“…Moreover, the sparsity of LiDAR beam in the vertical direction made LiDAR had insufficient vertical resolution. What's more, dew, artificial dirt, and foam would affect output data of LiDAR beam [18], and information loss would take place during vertical vibration [19]. Therefore, results of SLAM are easily to drift along the vertical direction.…”

mentioning

confidence: 99%

LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario

Yu,

Gong,

Zhao

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.

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Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Cited by 13 publications

References 24 publications

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives

Occlusion Model—A Geometric Sensor Modeling Approach for Virtual Testing of ADAS/AD Functions

LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario

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