The evolution of LiDAR and its application in high precision measurement

Wang, Xin; Pan, H; Guo, Kai; Xin-li, Yang; Luo, Sheng

doi:10.1088/1755-1315/502/1/012008

Cited by 33 publications

(20 citation statements)

References 11 publications

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“…Since the first laser rangefinder with a ruby laser was developed in 1961, laser raging has experienced ongoing development and has since become a mature and well-known active optical distance measuring technology. With the cost reduction of system composition and promising potential technical advances, laser ranging technology is playing a growing role in various applications [ 1 ]. Atmospheric studies and surface topography mapping are still mainstream applications of laser ranging [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Short-Range, High-Accuracy Compact Pulsed Laser Ranging System

Luo

et al. 2022

Sensors

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A short-range, compact, real-time pulsed laser rangefinder is constructed based on pulsed time-of-flight (ToF) method. In order to reduce timing discrimination error and achieve high ranging accuracy, gray-value distance correction and temperature correction are proposed, and are realized with a field programmable gate array (FPGA) in a real-time application. The ranging performances—such as the maximum ranging distance, the range standard deviation, and the ranging accuracy—are theoretically calculated and experimentally studied. By means of these proposed correction methods, the verification experimental results show that the achievable effective ranging distance can be up to 8.08 m with a ranging accuracy of less than ±11 mm. The improved performance shows that the designed laser rangefinder can satisfy on-line ranging applications with high precision, fast ranging speed, small size, and low implementation cost, and thus has potential in the areas of robotics, manufacturing, and autonomous navigation.

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Section: Introductionmentioning

confidence: 99%

The Short-Range, High-Accuracy Compact Pulsed Laser Ranging System

Luo

et al. 2022

Sensors

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“…In addition to this, the hardware used in autonomous vehicles is continuously being improved and updated. This trend is clearly observable in LiDAR sensors, which are continually being manufactured with higher and higher resolutions [3]. Collecting and labelling datasets covering the long-tail of open-world scenarios for every new sensor generation is prohibitively timeconsuming and expensive [4].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Domain Gap in LiDAR Object Detection Networks

Richter¹,

Faion²,

Feng³

et al. 2022

Preprint

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In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate training datasets which accurately represent this domain. Therefore, there are always domain gaps between training datasets and the open-world which must be understood. In this work, we investigate the domain gaps between high-resolution and low-resolution LiDAR sensors in object detection networks. Using a unique dataset, which enables us to study sensor resolution domain gaps independent of other effects, we show two distinct domain gaps -an inference domain gap and a training domain gap. The inference domain gap is characterised by a strong dependence on the number of LiDAR points per object, while the training gap shows no such dependence. These findings show that different approaches are required to close these inference and training domain gaps.

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“…The laser beam is generated to scan the measured area, when a wall or object is hit by a laser beam, the laser light will bounce back to the sensor and the sensor device will calculate the detected distance by measuring the time difference between sending the signal and receiving the signal. Because of these characteristics LiDAR is considerably suitable in autonomous vehicle applications and other similar applications, which of course are also suitable for use as input on the SLAM method [3][4][5]. Currently the google cartographer method is well-known among researchers in the scope of autonomous robot.…”

Section: Introductionmentioning

confidence: 99%

Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

Fikri

Anifah

2021

Jurnal Teknologi

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The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

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The evolution of LiDAR and its application in high precision measurement

Cited by 33 publications

References 11 publications

The Short-Range, High-Accuracy Compact Pulsed Laser Ranging System

The Short-Range, High-Accuracy Compact Pulsed Laser Ranging System

Understanding the Domain Gap in LiDAR Object Detection Networks

Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

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