Low-Cost Retina-Like Robotic Lidars Based on Incommensurable Scanning

Liu, Zheng; Fu, Zhang; Hong, Xiaoping

doi:10.1109/tmech.2021.3058173

Cited by 73 publications

(57 citation statements)

References 37 publications

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“…It produces two echoes with an 8 bit intensity. The scan pattern changes over time in a cyclic design so that, after a certain amount of time, the totality of the FOV is scanned [24]. Contrary to spinning lidars, over time, Risley prisms lidars scan the whole environment when they remain static.…”

Section: Lidar Sensorsmentioning

confidence: 99%

A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

et al. 2021

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Light Detection And Ranging sensors (lidar) are key to autonomous driving, but their data is severely impacted by weather events (rain, fog, snow). To increase the safety and availability of self-driving vehicles, the analysis of the phenomena of the consequences at stake is necessary. This paper presents experiments performed in a climatic chamber with lidars of different technologies (spinning, Risley prisms, micro-motion and MEMS) that are compared in various artificial rain and fog conditions. A specific target with calibrated reflectance is used to make a first quantitative analysis. We observe different results depending on the sensors, and unexpected behaviors in the analysis with artificial rain are seen where higher rain rates do not necessarily mean higher degradations on lidar data.

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Section: Lidar Sensorsmentioning

confidence: 99%

A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

et al. 2021

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“…Though each configuration follows the same generalized mathematical form for the direction of the emergent beam, selecting the correct configuration for the specific Risley prism mechanism being studied is important. Based on the information presented in [ 16 ], the Mid-40 Risley prism configuration is PA-AP.…”

Section: Methodsmentioning

confidence: 99%

“…Liu et al [ 16 ] state that the Mid-40 is composed of two identical prisms with a refractive index of 1.51, wedge angles of 18

, and angular velocities of

4664 revolutions per minute (RPM) and 7294 RPM for prisms A and B, respectively, following a right-hand rule for positive rotation around the X coordinate axis. However, the refractive index of air, the refractive index of the prisms, and the wedge angle of the prisms model parameters are perfectly correlated due to the use of Snell’s law within the observation model.…”

Section: Methodsmentioning

confidence: 99%

“…The rotation rates for the glass prisms inside the Mid-40 are set so the lidar observations are not repetitively taken along a single projected path within the FoV, rather the observation density within the FoV increases with time ( Figure 2 b). The observation density within the FoV is nonhomogeneous with a peak at the center, similar to the response of the human retina [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…In comparison to the laborious manufacturing techniques used for mechanical multi-line lidar sensors, the Mid-40 is mass produced using a straightforward approach which enables the sensor to be sold at a lower cost [ 16 ]. In addition to the lower cost, the reported accuracy specifications and physical properties of the sensor make it an attractive option for UAS lidar mapping applications ( Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Rigorous Observation Model for the Risley Prism-Based Livox Mid-40 Lidar Sensor

Brazeal

Wilkinson

Hochmair

2021

Sensors

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Modern lidar sensors are continuing to decrease in size, weight, and cost, but the demand for fast, abundant, and high-accuracy lidar observations is only increasing. The Livox Mid-40 lidar sensor was designed for use within sense-and-avoid navigation systems for autonomous vehicles, but has also found adoption within aerial mapping systems. In order to characterize the overall quality of the point clouds from the Mid-40 sensor and enable sensor calibration, a rigorous model of the sensor’s raw observations is needed. This paper presents the development of an angular observation model for the Mid-40 sensor, and its application within an extended Kalman filter that uses the sensor’s data to estimate the model’s operating parameters, systematic errors, and the instantaneous prism rotation angles for the Risley prism optical steering mechanism. The analysis suggests that the Mid-40’s angular observations are more accurate than the specifications provided by the manufacturer. Additionally, it is shown that the prism rotation angles can be used within a planar constrained least-squares adjustment to theoretically improve the accuracy of the angular observations of the Mid-40 sensor.

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Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

Wei

Chen

et al. 2023

Advanced Intelligent Systems

Self Cite

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Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high‐bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point‐LIO has two key novelties. The first one is a point‐by‐point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high‐frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in‐frame motion distortion. The second one is a stochastic process‐augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real‐world experiments are conducted for performance evaluation. Overall, Point‐LIO is capable to provide accurate, high‐frequency odometry (4–8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s−1) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point‐LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point‐LIO are demonstrated, one is a racing drone and the other is a self‐rotating unmanned aerial vehicle, both have aggressive motions.

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Low-Cost Retina-Like Robotic Lidars Based on Incommensurable Scanning

Cited by 73 publications

References 37 publications

A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

A Rigorous Observation Model for the Risley Prism-Based Livox Mid-40 Lidar Sensor

Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

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