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3 results on '"vision sensors for intelligent vehicles"'

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

Author

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

Subjects
Automated vehicle, measurement errors, vibration measurement, vision sensors for intelligent vehicles, Transportation engineering, TA1001-1280, Transportation and communications, HE1-9990
Abstract

Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar’s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s2 in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar’s IMU.

Published
2022
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2. IEEE Open Journal of Intelligent Transportation Systems / Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and Effects on the Point Cloud

Author

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

Abstract

Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar’s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s 2 in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar’s IMU., Version of record

Published
2022
Full Text
View/download PDF

3. GNSS/INS/visual deeply coupled integration: Preliminary investigation on dynamic jammed datasets

Author

Laura Ruotsalainen, Calogero Cristodaro, and Fabio Dovis

Subjects
Vision sensors for Intelligent Vehicles, GNSS, Computer science, Test procedures, Real-time computing, Navigation system, Jamming, GNSS, Vision sensors for Intelligent Vehicles, Information Fusion, Kalman Filter, Record and Replay, SDR, Electromagnetic interference, Record and Replay, Robustness (computer science), GNSS applications, Satellite navigation, SDR, Kalman Filter, Inertial navigation system, Information Fusion
Abstract

As the Global Satellite Navigation Systems (GNSS) signals are received and processed by the GNSS receivers with an extremely low power, they are vulnerable to non-intentional and intentional interference. In particular, the intentional transmission of Radio Frequency interference, known as jamming, can completely prevent the operation of GNSS receivers. The integration of measurements from multiple sensors with complementary error characteristics offers a possible countermeasure against jamming and, therefore, it can provide a position solution that has sufficient accuracy for the applications. This paper shows how a Deeply Coupled (DC) integration of GNSS with Inertial Navigation Systems (INS) and visual sensors enhances the robustness of the navigation system in presence of jamming. In particular, the effect of DC integration for jamming mitigation is investigated for a dynamic scenario where jamming signals have been added to a pre-recorded GNSS scenario by using the record and replay approach, emulating different interference levels. The results presented in this paper show that the deep coupling of GNSS with INS and visual sensors can sustain the navigation in presence of high power jamming, although such a strong interference completely masks the GNSS signal denying the navigation of a GNSS stand-alone system. Moreover, the test procedures suitability of the record and replay approach for the jamming scenario generation and combination with real datasets are discussed and analyzed.

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