Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and Effects on the Point Cloud

Schlager, Birgit; Goelles, Thomas; Behmer, Marco; Muckenhuber, Stefan; Payer, Johann; Watzenig, Daniel

doi:10.1109/ojits.2022.3176471

Cited by 18 publications

(11 citation statements)

References 15 publications

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“…Sensor-level corruptions: The sensors, when affected by numerous internal or external factors (e.g., sensor vibration [49], lighting conditions [25,34] and reflective materials), can induce various kinds of corruptions to the captured data. Based on prior discussions on sensor noises [3,7,25,47], we design 10 practical sensor-level corruptions-7 for point clouds and 3 for images.…”

Section: Corruptions In 3d Object Detectionmentioning

confidence: 99%

Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving

Dong¹,

Kang²,

Zhang³

et al. 2023

Preprint

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3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous driving systems. To comprehensively and rigorously benchmark the corruption robustness of 3D detectors, in this paper we design 27 types of common corruptions for both LiDAR and camera inputs considering real-world driving scenarios. By synthesizing these corruptions on public datasets, we establish three corruption robustness benchmarks-KITTI-C, nuScenes-C, and Waymo-C. Then, we conduct large-scale experiments on 24 diverse 3D object detection models to evaluate their corruption robustness. Based on the evaluation results, we draw several important findings, including: 1) motion-level corruptions are the most threatening ones that lead to significant performance drop of all models; 2) LiDAR-camera fusion models demonstrate better robustness; 3) camera-only models are extremely vulnerable to image corruptions, showing the indispensability of LiDAR point clouds. We release the benchmarks and codes at https://github.com/kkkcx/ 3D_Corruptions_AD. We hope that our benchmarks and findings can provide insights for future research on developing robust 3D object detection models.

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Section: Corruptions In 3d Object Detectionmentioning

confidence: 99%

Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving

Dong¹,

Kang²,

Zhang³

et al. 2023

Preprint

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“…A 46 × 46 cm aluminum sheet mounted on a construction of aluminum profiles served as the carrier of our target material. In previous studies, we used retroreflective targets [19], [20]. According to the new datasheet of the OS1-64, the range accuracy is lower for retroreflective targets than for diffuse targets [18].…”

Section: Targetmentioning

confidence: 99%

“…Furthermore, we extracted the ideal target by using plane segmentation. Following on from this, we calculated the distances between the measured points on the target and the ideal target similar to our previous studies [19], [20].…”

Section: Os1-64 Data Analyticsmentioning

confidence: 99%

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

Schlager

Goelles

Muckenhuber

et al. 2022

IEEE Open J. Intell. Transp. Syst.

Self Cite

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Lidar sensors play an essential role in the perception system of automated vehicles. Fault Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64 was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead to almost complete sensor blindness. The results look promising for further studies on fault detection and isolation, since the different contamination types lead to different symptom combinations.

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“…Accelerometers and optomechanical acceleration sensors (OMASs) [1,2] are widely used in various applications, starting from stationary and the very low-frequency range for geodesy and space navigation [3] to metrology and vibration tests with frequencies up to several kilohertz [4][5][6]. For acceleration measurements with the help of OMAS, a Fabry-Perot optical cavity is used.…”

Section: Introductionmentioning

confidence: 99%

Designs of Optomechanical Acceleration Sensors with the Natural Frequency from 5 Hz to 50 kHz

Rezinkina,

Braxmaier

2024

Designs

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In many applications, such as space navigation, metrology, testing, and geodesy, it is necessary to measure accelerations with frequencies ranging from fractions of a hertz to several kilohertz. For this purpose, optomechanical sensors are used. The natural frequency of such sensors should be approximately ten times greater than the frequency of the measured acceleration. In the case of triaxial acceleration measurements, a planar design with two sensors that measure accelerations in two perpendicular in-plane directions and a third sensor that measures out-of-plane acceleration is effective. The mechanical characteristics of the existing designs of both in-plane and out-of-plane types of sensors were analyzed, and the improved designs were elaborated. Using numerical simulation, the dependencies of the natural frequency level in the range from several hertz to tens of kilohertz on the designs and geometric parameters of opto-mechanical accelerometers were modeled. This allows one to select the accelerometer design and its parameters to measure the acceleration at the assigned frequency. It is shown that the opto-mechanical accelerometers of the proposed designs have reduced dissipation losses and crosstalk.

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Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and Effects on the Point Cloud

Cited by 18 publications

References 15 publications

Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving

Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving

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

Designs of Optomechanical Acceleration Sensors with the Natural Frequency from 5 Hz to 50 kHz

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