Genetic Algorithm for the Optimal LiDAR Sensor Configuration on a Vehicle

Berens, Felix; Elser, Stefan; Reischl, Markus

doi:10.1109/jsen.2021.3136362

Cited by 12 publications

(14 citation statements)

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“…The sensor configuration includes the position and rotation (yaw, pitch, and roll) of the sensors on a vehicle. A performance measure for the configuration and an algorithm to optimize the configuration for motorized optomechanical Li-DAR sensors is described in [18]. The optimization algorithm is based on a genetic algorithm, which can simultaneously optimize multiple sensors, by mutation and recombination steps.…”

Section: Sensor Configurationmentioning

confidence: 99%

“…where n(i, j) is the number of sensors that occupy the (i, j)-th subarea and L is the number of sensors (see [18] for a detailed description how to use these function).…”

Section: Sensor Configurationmentioning

confidence: 99%

“…Furthermore, LiDAR sensors are expensive. Moreover, the performance of the sensors regarding these methods depends on the position and rotation (yaw, pitch, and roll), also called sensor configuration, of the sensors on the vehicle [14][15][16][17][18]. Optimizing methods use sensor data to measure the performance of the sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Optimizing methods use sensor data to measure the performance of the sensors. [17,18]. In the optimization methods the sensor configuration has to be changed multiple time.…”

Section: Introductionmentioning

confidence: 99%

“…To demonstrate the application of our simulation of MEMS LiDAR sensors, we optimized the sensor configuration for MEMS LiDAR sensors using the method described in [18].…”

Section: Introductionmentioning

confidence: 99%

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Generation of synthetic Point Clouds for MEMS LiDAR Sensor

Berens¹,

Reischl²,

Elser³

2022

Preprint

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Sensory data is essential for the training of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative is simulated data, but for MEMS LiDAR sensors there is no publicly available software to simulate this type of sensor. With this paper, we introduce a method to simulate data recorded by a MEMS LiDAR sensor like the Blickfeld Cube~1 (and other MEMS LiDAR sensors as well). For this, we use the open-source autonomous driving simulation environment CARLA (our method is available online\footnote[1]{https://github.com/BerensRWU/MEMS-LiDAR-Generator}). We compare our synthetic point cloud with a real-world point cloud and evaluate the similarity. Moreover, we demonstrate the application of our method on the problem of the optimal sensor configuration.

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Section: Sensor Configurationmentioning

confidence: 99%

“…where n(i, j) is the number of sensors that occupy the (i, j)-th subarea and L is the number of sensors (see [18] for a detailed description how to use these function).…”

Section: Sensor Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Optimizing methods use sensor data to measure the performance of the sensors. [17,18]. In the optimization methods the sensor configuration has to be changed multiple time.…”

Section: Introductionmentioning

confidence: 99%

“…To demonstrate the application of our simulation of MEMS LiDAR sensors, we optimized the sensor configuration for MEMS LiDAR sensors using the method described in [18].…”

Section: Introductionmentioning

confidence: 99%

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