Traditional Lidar Research Articles

Combining geometrical and intensity information to recognize vehicles from super-high density UAV-LiDAR point clouds

The traditional airborne LiDAR point clouds vehicle recognition algorithms only utilize the contained geometric information and are not sufficient to recognize vehicles accurately, especially in complex urban scenes. And their applicability in super-high density UAV-LiDAR point clouds is unknown. Therefore, a 3-D algorithm combining geometric and intensity information from super-high density UAV-LiDAR point clouds is presented. The proposed algorithm firstly converts the original point clouds into a 3D multivalued image which fuses intensity, elevation and density information simultaneously. Thereafter, the potential vehicle voxels are extracted according to the intensity, elevation and density consistency of vehicles. Subsequently, individual vehicles are recognized using the potential vehicle voxel’s spatially connected set with vehicle size constraint. Finally, the quantified attribute information of each individual vehicle, containing the spatial location, type, and size, is determined. The results for recognized vehicles are evaluated using UAV-LiDAR data with different densities and demonstrate an average quality (Kappa coefficient) of 96.58% (96.04%) without being significantly affected by occlusion and the very close vehicle arrangement.

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

Abstract Simultaneous localization and mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR inertial odometry approach integrating inertial measurement units and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on fast point feature histograms feature descriptors for coarse alignment, providing initial estimates for the generalized iterative closest point algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.

M-GCLO: Multiple Ground Constrained LiDAR Odometry

Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.

A Direct Time-of-Flight Image Sensor With In-Pixel Surface Detection and Dynamic Vision

3D flash LIDAR is an alternative to the traditional scanning LIDAR systems, promising precise depth imaging in a compact form factor, and free of moving parts, for applications such as self-driving cars, robotics and augmented reality (AR). Typically implemented using single-photon, direct time-of-flight (dToF) receivers in image sensor format, the operation of the devices can be hindered by the large number of photon events needing to be processed and compressed in outdoor scenarios, limiting frame rates and scalability to larger arrays. We here present a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$64 \times 32$</tex-math></inline-formula> pixel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$256 \times 128$</tex-math></inline-formula> SPAD) dToF imager that overcomes these limitations by using pixels with embedded histogramming, which lock onto and track the return signal. This reduces the size of output data frames considerably, enabling maximum frame rates in the 10 kFPS range or 100 kFPS for direct depth readings. The sensor offers selective readout of pixels detecting surfaces, or those sensing motion, akin to dynamic vision sensors that report changes in light intensity, leading to reduced power consumption and off-chip processing requirements. We demonstrate the application of the sensor in mid-range LIDAR.

An intensity-enhanced LiDAR SLAM for unstructured environments

Traditional LiDAR simultaneous localization and mapping (SLAM) methods rely on geometric features such as lines and planes to estimate pose. However, in unstructured environments where geometric features are sparse or absent, point cloud registration may fail, resulting in decreased mapping and localization accuracy of the LiDAR SLAM system. To overcome this challenge, we propose a comprehensive LiDAR SLAM framework that leverages both geometric and intensity information, specifically tailored for unstructured environments. Firstly, we adaptively extract intensity features and construct intensity constraints based on degradation detection, and then propose a multi-resolution intensity map construction method. The experimental results show that our method achieves a 55% accuracy improvement over the pure geometric LiDAR SLAM system and exhibits superior anti-interference capability in urban corner scenarios. Compared with Intensity-SLAM, the advanced intensity-assisted LiDAR SLAM, our method achieves higher accuracy and efficiency.

A LiDAR-intensity SLAM and loop closure detection method using an intensity cylindrical-projection shape context descriptor

LiDAR Simultaneous Localization and Mapping (SLAM) is able to map unknown scene while online estimating the LiDAR’s pose. Traditional LiDAR SLAM frameworks mainly rely on geometric features in the environments, but ignore LiDAR intensity information, leading to low accuracy in scenes with sparse environmental features. This paper proposes a novel intensity-based LiDAR-SLAM framework. In the front-end, the geometric and intensity features are extracted to match the two consecutive scans. To keep time efficiency, a self-adaptive feature selection strategy is proposed to select geometric and intensity features and the 6 DOF transformation from the corresponding features are weighted fused for odometry estimation. To improve the effectiveness of loop closure detection (LCD), we propose a novel intensity cylindrical-projection shape context (ICPSC) descriptor and a row-column similarity estimation based on ICPSC. To guarantee the accuracy of LCD, a double-value loop candidate verification strategy is employed. We have conducted comprehensive experimental verification of our LiDAR SLAM framework, including both the public KITTI datasets and data from our platform that involve multiple scenes. The results display that our framework achieves an averaged improvement of about 3 m in scenes with sparse environmental features in terms of on-line localization relative to LeGO-LOAM, while only increases maximum 9 ms in terms of time cost.

Vision-Aided Hyperspectral Full-Waveform LiDAR System to Improve Detection Efficiency

The hyperspectral full-waveform LiDAR (HSL) system based on the supercontinuum laser can obtain spatial and spectral information of the target synchronously and outperform traditional LiDAR or imaging spectrometers in target classification and other applications. However, low detection efficiency caused by the detection of useless background points (ULBG) hinders its practical applications, especially when the target is small compared with the large field of view (FOV) of the HSL system. A novel vision-aided hyperspectral full-waveform LiDAR system (V-HSL) was proposed to solve the problem and improve detection efficiency. First, we established the framework and developed preliminary algorithms for the V-HSL system. Next, we experimentally compared the performance of the V-HSL system with the HSL system. The results revealed that the proposed V-HSL system could reduce the detection of ULBG points and improve detection efficiency with enhanced detection performance. The V-HSL system is a promising development direction, and the study results will help researchers and engineers develop and optimize their design of the HSL system and ensure high detection efficiency of spatial and spectral information of the target.

Versatile LiDAR-Inertial Odometry With SE(2) Constraints for Ground Vehicles

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.

SIMULATION OF LOW-COST MEMS-LIDAR AND ANALYSIS OF ITS EFFECT ON THE PERFORMANCES OF STATE-OF-THE-ART SLAMS

Abstract. Indoor Unmanned Aerial Vehicles have often been tasked with performing SLAM, and the sensors most used in literature and industry have been cameras. Spanning from stereo to event cameras, visual algorithms have often been the de facto choice for localization. While visual SLAM has reached a high level of accuracy in localization, accurate map reconstruction still proves to be challenging. Meanwhile, LiDAR sensors have been used for years to obtain accurate maps. First in surveying applications and in the past ten years in the automotive sector. The weight, power, and size constraints of most traditional LiDARs have prevented their installation on UAVs for indoor use. MEMS-based LiDARs have already been used in UAVs but had to rely on algorithms designed to deal with their small FOV. Recently, a MEMS-based LiDAR with a wide field of view (360°*59°) and weighing 265 g has sparked interest in its potential for indoor UAV SLAM. We performed an extensive battery of tests in simulation environments to provide a first look into its effect on state-of-the-art SLAM algorithms, highlight which ones can provide the best results, and what improvements may be most beneficial. This paper aims to provide assistance in further research in the field by releasing the tool used for this work.

Si Photonics FMCW LiDAR Chip with Solid-State Beam Steering by Interleaved Coaxial Optical Phased Array.

LiDAR has attracted increasing attention because of its strong anti-interference ability and high resolution. Traditional LiDAR systems rely on discrete components and face the challenges of high cost, large volume, and complex construction. Photonic integration technology can solve these problems and achieve high integration, compact dimension, and low-cost on-chip LiDAR solutions. A solid-state frequency-modulated continuous-wave LiDAR based on a silicon photonic chip is proposed and demonstrated. Two sets of optical phased array antennas are integrated on an optical chip to form a transmitter-receiver interleaved coaxial all-solid-state coherent optical system which provides high power efficiency, in principle, compared with a coaxial optical system using a 2 × 2 beam splitter. The solid-state scanning on the chip is realized by optical phased array without a mechanical structure. A 32-channel transmitter-receiver interleaved coaxial all-solid-state FMCW LiDAR chip design is demonstrated. The measured beam width is 0.4° × 0.8°, and the grating lobe suppression ratio is 6 dB. Preliminary FMCW ranging of multiple targets scanned by OPA was performed. The photonic integrated chip is fabricated on a CMOS-compatible silicon photonics platform, providing a steady path to the commercialization of low-cost on-chip solid-state FMCW LiDAR.

Validation of Solid-State LiDAR Measurement System for Ballast Geometry Monitoring in Rail Tracks

The inspection and maintenance of track ballast are fundamental tasks for the preservation of the condition of railway networks. This work presents an application based on a low-cost solid-state LiDAR system, which allows the user to accurately measure the ballast geometry from a mobile inspection trolley or draisine. The solid-state LiDAR system, the LiVOX Avia, was validated on a test track through comparison with a traditional static LiDAR system, the Faro Focus 3D. The results show a standard deviation of around 6 mm for the solid-state LiDAR system. The LiVOX system also provides the capability to measure the ballast digital elevation model and profiles. The LiVOX results are in agreement with those obtained from the Faro Focus. The results demonstrate that the LiVOX system can sufficiently measure even the displacement of a single layer of ballast stones typically between 2.5 cm and 5 cm. The data provided can be easily digitalized using image processing tools and integrated into geographic information systems for infrastructure management.

InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be unreliable in dynamic or degraded environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of lazer sweeps (i.e., geometric, intensity and temporal characteristics). The specific content of this work includes method innovation and experimental verification. With respect to method innovation, we propose the cylindrical-image-based feature extraction scheme, which makes use of the characteristic of uniform spatial distribution of lazer points to boost the adaptive extraction of various types of features, i.e., ground, beam, facade and reflector. We propose a novel intensity-based point registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out in the resulting points map. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. With respect to experimental verification, extensive tests are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy with respect to the state-of-the-art in normal driving scenarios and outperforms geometric-based LO in unstructured environments.

Design of Monolithic 2D Optical Phased Arrays Heterogeneously Integrated with On-Chip Laser Arrays Based on SOI Photonic Platform.

In this work, heterogeneous integration of both two-dimensional (2D) optical phased arrays (OPAs) and on-chip laser arrays based on a silicon photonic platform is proposed. The tunable multi-quantum-well (MQW) laser arrays, active switching/shifting arrays, and grating antenna arrays are used in the OPA module to realize 2D spatial beam scanning. The 2D OPA chip is composed of four main parts: (1) tunable MQW laser array emitting light signals in the range of 1480-1600 nm wavelengths; (2) electro-optic (EO) switch array for selecting the desired signal light from the on-chip laser array; (3) EO phase-shifter array for holding a fixed phase difference for the uniform amplitude of specific optical signal; and (4) Bragg waveguide grating antenna array for controlling beamforming. By optimizing the overall performances of the 2D OPA chip, a large steering range of 88.4° × 18° is realized by tuning both the phase and the wavelength for each antenna. In contrast to the traditional thermo-optic LIDAR chip with an external light source, the overall footprint of the 2D OPA chip can be limited to 8 mm × 3 mm, and the modulation rate can be 2.5 ps. The ultra-compact 2D OPA assembling with on-chip tunable laser arrays using hybrid integration could result in the application of a high-density, high-speed, and high-precision lidar system in the future.

Automatic targetless LiDAR–camera calibration: a survey

The recent trend of fusing complementary data from LiDARs and cameras for more accurate perception has made the extrinsic calibration between the two sensors critically important. Indeed, to align the sensors spatially for proper data fusion, the calibration process usually involves estimating the extrinsic parameters between them. Traditional LiDAR–camera calibration methods often depend on explicit targets or human intervention, which can be prohibitively expensive and cumbersome. Recognizing these weaknesses, recent methods usually adopt the autonomic targetless calibration approach, which can be conducted at a much lower cost. This paper presents a thorough review of these automatic targetless LiDAR–camera calibration methods. Specifically, based on how the potential cues in the environment are retrieved and utilized in the calibration process, we divide the methods into four categories: information theory based, feature based, ego-motion based, and learning based methods. For each category, we provide an in-depth overview with insights we have gathered, hoping to serve as a potential guidance for researchers in the related fields.

A novel approach to solve forward/inverse problems in remote sensing applications

Inversion of electromagnetic (EM) signals reflected from or transmitted through a medium, or emitted by it due to internal sources can be used to investigate the optical and physical properties of a variety of scattering/absorbing/emitting materials. Such media encompass planetary atmospheres and surfaces (including water/snow/ice), and plant canopies. In many situations the signals emerging from such media can be described by a linear transport equation which in the case of EM radiation is the radiative transfer equation (RTE). Solutions of the RTE can be used as a forward model to solve the inverse problem to determine the medium state parameters giving rise to the emergent (reflected/transmitted/emitted) EM signals. A novel method is developed to determine layer-by-layer contributions to the emergent signals from such stratified, multilayered media based on the solution of the pertinent RTE. As a specific example of how this approach may be applied, the radiation reflected from a multilayered atmosphere is used to solve the problem relevant for EM probing by a space-based lidar system. The solutions agree with those obtained using the standard lidar approach for situations in which single scattering prevails, but this novel approach also yields reliable results for optically thick, multiple scattering aerosol and cloud layers that cannot be provided by the traditional lidar approach.

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a hand-held sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-of-the-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors. The dataset is accessible at the following url: https://rmc.dlr.de/s3li_dataset

A Progress Review on Solid‐State LiDAR and Nanophotonics‐Based LiDAR Sensors

AbstractLight detection and ranging (LiDAR) sensors enable precision sensing of an object in 3D. LiDAR technology is widely used in metrology, environment monitoring, archaeology, and robotics. It also shows high potential to be applied in autonomous driving. In traditional LiDAR sensors, mechanical rotator is used for optical beam scanning, which brings about limitations on their reliability, size, and cost. These limitations can be overcome by a more compact solid‐state solution. Solid‐state LiDAR sensors are commonly categorized into the following three types: flash‐based LiDAR, microelectromechanical system (MEMS)‐based LiDAR, and optical phased array (OPA)‐based LiDAR. Furthermore, advanced optics technology enables novel nanophotonics‐based devices with high potential and superior advantages to be utilized in a LiDAR sensor. In this review, LiDAR sensor principles are introduced, including three commonly used sensing schemes: pulsed time of flight (TOF), amplitude‐modulated continuous wave TOF, and frequency‐modulated continuous wave. Recent advances in conventional solid‐state LiDAR sensors are summarized and presented, including flash‐based LiDAR, MEMS‐based LiDAR, and OPA‐based LiDAR. The recent progress on emerging nanophotonics‐based LiDAR sensors is also covered. A summary is made and the future outlook on advanced LiDAR sensors is provided.

Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery

The Full Waveform LiDAR system has been developed and used commercially all over the world. It acts to record the complete time of a laser pulse and has a high-resolution sampling interval compared to the traditional multiple-echo LiDAR, which only provides signals within a single target range. This study area mainly collects data from Riegl LMS-Q680i Full Waveform LiDAR and WorldView-2 satellite imagery, which focuses on buildings, vegetation, grassland, asphalt roads and other ground types as the surface objects. The amplitude and pulse width are selected as waveform basic parameters. The parameter of topography is slope, and the height classification parameters of the test ground are 0–0.5 m, 0.5–2.5 m, and 2.5 m. To eliminate noise, the neighborhood average is applied on the LiDAR parameter values and analyzed as the classification accuracy comparison. This survey uses Decision Tree as the classification method. Comparing the data between neighborhood average and non-neighborhood average, the data classification accuracy improves by 7%, and Kappa improves by 5.92%. NDVI image data are utilized to distinguish the artificial from natural ground. The results show that the neighborhood average with previous data can improve the classification accuracy by 5%, and Kappa improves by 4.25%. By adding NIR-2 of WorldView-2 satellite imagery to the neighborhood average analysis, the overall classification accuracy is improved by 2%, and the Kappa value by 1.21%. This article shows that utilizing the analysis of neighborhood average and image parameters can effectively improve the classification accuracy of land covers.

N distribution characterization based on organ-level biomass and N concentration using a hyperspectral lidar

Accurate estimates of the N concentration and biomass (W) in plant organs provide information regarding the mechanisms of N distribution, which is critical for improving N use efficiency (NUE) and optimizing N management. Dimensionless passive remotely sensed data may lead to the asymptotic saturation problem when extracting W, whereas commercial lidar systems employing only one wavelength band have limited capacity in N retrieval. Combining the advantages of passive remote sensing and traditional lidar, hyperspectral lidar (HSL) has the ability to simultaneously extract structural and spectral information for plants. The objectives of this research were to evaluate the ability of HSL to estimate maize N concentration and W at the organ level, and to test whether HSL can characterize maize N distribution at different growth stages and under different N fertilizer conditions. A wide range of HSL performance for leaf and stem N extraction (R2 = 0.71 – 0.91) was observed based on the partial least squares regression (PLSR) method with spectral indices as inputs. Close relationships (R2 ≥ 0.75) were established between extracted height metrics (stem height and plant height) and organ-level W. The W portioning, dynamics of N concentration, and the variations in N with W accumulation were successfully monitored based on the estimated N concentration and W, thus demonstrating the capacity of HSL to characterize the N distribution pattern within maize plants. Our results show that the novel HSL system holds much potential for monitoring plant N distribution and serving precision agriculture.

EKF-LOAM: An Adaptive Fusion of LiDAR SLAM With Wheel Odometry and Inertial Data for Confined Spaces With Few Geometric Features

A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> , which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.