Analysis of optimal detection range performance of LiDAR systems applying coaxial optics

Kim, Duck-Lae; Park, Hyun-Woo; Yeon, Yoon-Mo

doi:10.1016/j.heliyon.2022.e12493

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Figure 11 shows some of the testing trajectories, while Table 4 shows the Euclidean distance travelled and the travel time for each of the models. In these scenarios, the robot starts from the default position (0.0, 0.0) to navigate to a goal point of (4,4). As shown in the results, Model 2's travel time and the distance travelled to get to its goal point are shorter compared to the other two models.…”

Section: Simulation Performance Evaluationmentioning

confidence: 86%

“…However, such visual mapping systems have two major limitations: (1) they cannot reliably identify obstacles in low light conditions or when dealing with repetitive patterns, and (2) processing visual data can be computationally intensive [ 3 ]. Unlike vSLAM, LiDAR uses eye-safe laser beams to capture the surrounding environment in 2D or 3D providing computing systems with an accurate representation of its environment that prompts its use by many automobile companies such as Volkswagen, Volvo, and Hyundai for autonomous driving, object detection, mapping, and localization [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework

Olayemi,

Van,

McLoone

et al. 2023

Sensors

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Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor’s width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot’s velocities, the robot’s orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model’s good performance and applicability in the real world.

show abstract

Section: Simulation Performance Evaluationmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework

Olayemi,

Van,

McLoone

et al. 2023

Sensors

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Design a long-range near infrared LiDAR imaging system for security and surveillance applications

Rao,

Chandra

et al. 2024

J Opt

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Design and experimental validation of an aspheric multi-lenticular plano-convex lens for VCSEL beam collimation

Madeniyeti,

Yi,

et al. 2024

Appl. Opt.

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In this work, we propose a design method of an aspheric lens that achieves collimation for a VCSEL laser beam. The designed lens features a planar front surface and an aspheric back surface of which the profile is mathematically characterized and precisely determined based on the proposed method. The method is derived from a basic geometric-optics analysis and construction approach. The collimating effect of the lens was first analyzed in simulation and then validated in experimental measurement. The experimental results show that the collimator lens transforms the input VCSEL laser beam divergence angle from 25° (436.33 mrad) to an output angle of 3.6906 mrad.

show abstract

Analysis of optimal detection range performance of LiDAR systems applying coaxial optics

Cited by 3 publications

References 12 publications

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework

Design a long-range near infrared LiDAR imaging system for security and surveillance applications

Design and experimental validation of an aspheric multi-lenticular plano-convex lens for VCSEL beam collimation

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