SPAD LiDAR: From Physical Properties to Signal Processing

<span lang="EN-US">In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.</span>

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

Simultaneous Localization and Mapping (SLAM) is critical for autonomous driving. Compared with visual SLAM which suffers from variable lighting conditions and sparse features, Light Detection and Ranging (LiDAR) based solution is highly competitive since LiDAR can offer precise distance measurements and robust resistance to changes in illumination. However, current LiDAR SLAM techniques only estimate pose from a single scale, such that complex environments with dynamic objects and similar features can heavily affect the performance, leading to poor accuracy and robustness of pose estimation. To address these challenges, we present MTC-SLAM, a multi-scale and tightly coupled LiDAR-Inertial SLAM method. It begins with a coarse adjustment across six degrees of freedom at a larger scale, followed by adaptive step-size least squares updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{y}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{yaw}$</tex-math></inline-formula> on a smaller scale, and dynamic interpolation updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{z}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{pitch}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{roll}$</tex-math></inline-formula>. The updated poses are integrated as odometer factors, alongside Global Positioning System (GPS) and loop closure factors, to construct a factor graph for backend optimization. Additionally, a multi-scale loop closure detection method is proposed to identify loops using multi-scale descriptors for global optimization. Extensive experiments on the MulRan dataset demonstrate that MTC-SLAM outperforms existing mainstream methods in both accuracy and robustness.

Light Detection and Ranging (LiDAR)-based simultaneous localization and mapping (SLAM) is a core technology for autonomous vehicles and robots. One key contribution of this work to 3D LiDAR SLAM and localization is a fierce defense of view-based maps (pose graphs with time-stamped sensor readings) as the fundamental representation of maps. As will be shown, they allow for the greatest flexibility, enabling the posterior generation of arbitrary metric maps optimized for particular tasks, for example, obstacle avoidance and real-time localization. Moreover, this work introduces a new framework in which mapping pipelines can be defined without coding, defining the connections of a network of reusable blocks much like deep-learning networks are designed by connecting layers of standardized elements. We also introduce tightly-coupled estimation of linear and angular velocity vectors within the Iterative Closest Point (ICP)-like optimizer, leading to superior robustness against aggressive motion profiles without the need for an IMU. Extensive experimental validation reveals that the proposal compares well to, or improves, former state-of-the-art (SOTA) LiDAR odometry systems, while also successfully mapping some hard sequences where others diverge. A proposed self-adaptive configuration has been used, without parameter changes, for all 3D LiDAR datasets with sensors between 16 and 128 rings, and has been extensively tested on 83 sequences over more than 250 km of automotive, hand-held, airborne, and quadruped LiDAR datasets, both indoors and outdoors. The system flexibility is demonstrated with additional configurations for 2D LiDARs and for building 3D NDT-like maps. The framework is open-sourced online: https://github.com/MOLAorg/mola.

Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.

A monolithic resistorless circuit has been designed for active reset of the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> P single-ended (common anode) single-photon avalanche diode (SPAD). This type of SPAD has enhanced the photon detection efficiency in the near-infrared (NIR) region relatively to the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> N (two-ended) SPAD. Due to this fact, the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> P SPAD is a better candidate for the light detection and range (LiDAR) working in the NIR region. The circuit is fabricated in a standard 0.18-μm CMOS image sensor process. The dead time after each photon detection is adjustable; the minimum measured value is 4 ns. The maximum photon count rate corresponding to this dead time is 2.5·10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> photons/s. This circuit guarantees precise repeatable response, short and well-controlled dead time, and after-pulsing effect reduction. All these are essential for many SPAD applications and crucial for increasing the saturation level of the silicon photo multiplier (SiPM)based LiDAR systems working in strong background light conditions on sunny day.

A high-precision mapping is one of the essential requirements for safe autonomous driving because it is directly connected with the accuracy and safety of autonomous driving. Due to the development of 3D light detection and ranging (LiDAR) sensor technology, LiDAR is playing a key role to create the precise maps. Although many open-source-based LiDAR simultaneous localization and mapping (SLAM) algorithms have been developed, their accuracy in actual experiments is often not proven. To handle this issue, our paper aims to provide background materials for autonomous robots and experimental validations of open-source-based LiDAR SLAM. In the experiments, the comparison results with four state-of-the-art LiDAR mapping algorithms are described including LiDAR odometry and mapping(LOAM) and LiDAR inertial odometry mapping.

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

The laser pulse detection probability of a scanning direct time-of-flight light detection and ranging (LiDAR) measurement is evaluated based on the optical signal distribution on a multipixel single photon avalanche diode (SPAD) array. These detectors intrinsically suffer from dead-times after the successful detection of a single photon and, thus, allow only for limited counting statistics when multiple returning laser photons are imaged on a single pixel. By blurring the imaged laser spot, the transition from single-pixel statistics with high signal intensity to multipixel statistics with less signal intensity is examined. Specifically, a comparison is made between the boundary cases in which (i) the returning LiDAR signal is focused through optics onto a single pixel and (ii) the detection is performed without lenses using all available pixels on the sensor matrix. The omission of imaging optics reduces the overall system size and minimizes optical transfer losses, which is crucial given the limited laser emission power due to safety standards. The investigation relies on a photon rate model for interfering (background) and signal light, applied to a simulated first-photon sensor architecture. For single-shot scenarios that reflect the optimal use of the time budget in scanning LiDAR systems, the lens-less and blurred approaches can achieve comparable or even superior results to the focusing system. This highlights the potential of fully solid-state scanning LiDAR systems utilizing optical phase arrays or multidirectional laser chips.

Single-photon avalanche diode (SPAD)-based light detection and ranging (LiDAR) introduces significant pileup errors at high echo photon flux due to the SPAD's dead time. To expand the application of single-photon LiDAR in the field of remote sensing, autonomous driving, etc., it is crucial to perform rapid and accurate ranging within an echo photon flux of a wide dynamic range. Emerging SPAD array detectors combined with specific photon detection modes attempt to strike a balance between photon detection efficiency and hardware resources, necessitating the development of appropriate dead time compensation methods. Therefore, we experimentally investigated a synchronous single-photon ranging LiDAR operating in a mode where up to two photons are detected per laser cycle, and proposed dead time compensation methods based on forward modeling of the photon detection process. The results show that, using a wide laser pulse of 3.5 ns, the compensated ranging error is less than 4.2 cm, while the uncompensated ranging error is as max as 17.96 cm. Our method provides a reference for dead time compensation in single-photon LiDAR that requires comprehensive consideration of detection efficiency and hardware resources.

This paper presents the comparison and analysis of LIDAR (Light Detection and Ranging)-based SLAM (Simultaneous Localization and Mapping) Frameworks such as Hector SLAM, GMapping and Karto SLAM in dynamic environments with moving objects. Many experiments have been conducted using a mobile robot equipped with a LIDAR, which is operated on ROS (Robot Operating System) in two indoor environments including a moving object. Experimental results showed that the results of the three SLAM frameworks were different according to the types and trajectories of the moving object.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical &amp; digital imagery information for various applications.

This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.

Autonomous service robots are gaining popularity as they find application in different sectors such as logistics, hospitality, medical, space exploration and military. It is widely seen that autonomous service robots help increase efficiency, reduce cost and ensure safer operations. Automating material handling operations in highly dynamic indoor environments is risky and involves use of complex algorithms. However, continuously advancing technologies and machine programming tend to overcome these challenges. The three essential functions of an autonomous robot are to identify its instantaneous location in three-dimensional space, generate the environment map and decide an optimal path for locomotion. Simultaneous Localization and Mapping (SLAM) is a widely used algorithm for navigation in unfamiliar environments. This chapter presents a cost-effective autonomous ground robot for automating material handling, transportation and other repetitive tasks in dynamically changing surroundings. The robot is programmed to map the indoor environment and localize itself in the workspace with the help of SLAM and Adaptive Monte Carlo Localization (AMCL). In addition, the robot can manoeuvre through the indoor workspace by avoiding static and dynamic obstacles. This work employs the GMapping open-source package in the Robot Operating System (ROS) for performing SLAM with the aid of a laser range finder, wheel odometry and inertial data. The GMapping algorithm uses a probabilistic approach to construct a map and estimate the robot’s location in two-dimensional space. In addition, this chapter presents an autonomous navigation feature with the help of the AMCL method for localizing the robot within an uncertain environment and making the robot move to its goal using move base actions in ROS. The path planning of the mobile robot is classified into global and local plans. The global plan is responsible for planning the path from the start point to the endpoint. The local plan is responsible for planning the actual movement of the robot by avoiding any dynamic obstacles not mapped earlier. This work uses the Dijistra algorithm for the global and the dynamic window approach (DWA) algorithm for the local path planning. The optimization algorithm minimizes the error between the global and local paths. The simulation study of the proposed algorithm is performed in a virtual environment called Gazebo in ROS, which is nearly similar to a real-time indoor workspace. Further, a robot prototype is fabricated, and its performance is evaluated in an unknown environment. The robot prototype is equipped with brushed DC motors with optical encoder feedback for wheel odometry (ODOM), Light Detection and Ranging (LiDAR), and Inertial Measurement Unit (IMU) sensors controlled using a Jetson Nano. The simulation and experimental results showcase the robot’s successful two-dimensional localization and navigation in the factory environment. The current system has the potential for indoor assistance during material transfer and logistic applications. Further upgrades to the system can make it usable for Automated Storage and Retrieval System (ASRS) applications in warehouses/factories.

Performance of systems for optical detection depends on the choice of the right detector for the right application. Designers of optical systems for ranging applications can choose from a variety of highly sensitive photodetectors, of which the two most prominent ones are linear mode avalanche photodiodes (LM-APDs or APDs) and Geiger-mode APDs or single-photon avalanche diodes (SPADs). Both achieve high responsivity and fast optical response, while maintaining low noise characteristics, which is crucial in low-light applications such as fluorescence lifetime measurements or high intensity measurements, for example, Light Detection and Ranging (LiDAR), in outdoor scenarios. The signal-to-noise ratio (SNR) of detectors is used as an analytical, scenario-dependent tool to simplify detector choice for optical system designers depending on technologically achievable photodiode parameters. In this article, analytical methods are used to obtain a universal SNR comparison of APDs and SPADs for the first time. Different signal and ambient light power levels are evaluated. The low noise characteristic of a typical SPAD leads to high SNR in scenarios with overall low signal power, but high background illumination can saturate the detector. LM-APDs achieve higher SNR in systems with higher signal and noise power but compromise signals with low power because of the noise characteristic of the diode and its readout electronics. Besides pure differentiation of signal levels without time information, ranging performance in LiDAR with time-dependent signals is discussed for a reference distance of 100 m. This evaluation should support LiDAR system designers in choosing a matching photodiode and allows for further discussion regarding future technological development and multi pixel detector designs in a common framework.