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[IEEE 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Singapore, Singapore (2018.11.18-2018.11.21)] 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Map Comparison of Lidar-based 2D SLAM Algorithms Using Precise Ground Truth
摘要： This paper presents a comparative analysis of three most common ROS-based 2D Simultaneous Localization and Mapping (SLAM) libraries: Google Cartographer, Gmapping and Hector SLAM, using a metric of average distance to the nearest neighbor (ADNN). Each library was applied to construct a map using data from 2D lidar that was placed on an autonomous mobile robot. All the approaches have been evaluated and compared in terms of inaccuracy constructed maps against the precise ground truth presented by FARO laser tracker in static indoor environment.
关键词： ground truth,ADNN,lidar,ROS,map comparison,SLAM
更新于2025-09-23 15:23:52
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT) - Busan, Korea (South) (2018.9.6-2018.9.8)] 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT) - Global Map Generation using LiDAR and Stereo Camera for Initial Positioning of Mobile Robot
摘要： A lot of researches have been made on how to know the position of the mobile robot when it knows the initial position of the mobile robot. For the robot to be completely unmanned, the robot also needs to find out its own initial position. To do this, it is necessary to obtain enough data to estimate the initial position without previous data. In Simultaneous Localization and Mapping (SLAM), Light Detection and Ranging (LiDAR) is often used to obtain accurate map for mobile robot. In this case, it is difficult to find the initial position because there is little information at initial start-up. On the other hand, stereo camera has the advantage that it can acquire more information than LiDAR by acquiring spatial information with the same principle as the human eye. However, the obtained 3D spatial information has a disadvantage of low precision. The purpose of this paper is to form a 3D map to be used for finding the initial position by linking LRF information to compensate for the low accuracy of the 3D map made only by the stereo camera in the environment.
关键词： 3D Map,Stereo Camera,LiDAR,Initial Positioning,SLAM
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Gaussian Decomposition of LiDAR Waveform Data Simulated by Dart
摘要： Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.
关键词： point cloud,DART,waveform,LiDAR,ALS,Gaussian decomposition,TLS
更新于2025-09-23 15:23:52
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Lane-Level Localization and Mapping in GNSS-Challenged Environments by Fusing Lidar Data and Cellular Pseudoranges
摘要： A method for achieving lane-level localization in global navigation satellite system (GNSS)-challenged environments is presented. The proposed method uses the pseudoranges drawn from unknown ambient cellular towers as an exclusive aiding source for a vehicle-mounted light detection and ranging (lidar) sensor. The following scenario is considered. A vehicle aiding its lidar with GNSS signals enters an environment where these signals become unusable. The vehicle is equipped with a receiver capable of producing pseudoranges to unknown cellular towers in its environment. These pseudoranges are fused through an extended Kalman filter (EKF) to aid the lidar odometry, while estimating the vehicle’s own state (three-dimensional position and orientation) simultaneously with the position of the cellular towers and the difference between the receiver’s and cellular towers’ clock error states (bias and drift). The proposed method is computationally efficient and is demonstrated to achieve lane-level accuracy in different environments. Simulation and experimental results with the proposed method are presented illustrating a close match between the vehicle’s true trajectory and that estimated using the cellular-aided lidar odometry over a 1 km trajectory. A 60% reduction in localization error is obtained over the lidar odometry-only approach.
关键词： Lidar,Signals of opportunity,SLAM,Cellular
更新于2025-09-23 15:23:52
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Sensor Fusion and Registration of Lidar and Stereo Camera without Calibration Objects
摘要： Environment perception is an important task for intelligent vehicles applications. Typically, multiple sensors with different characteristics are employed to perceive the environment. To robustly perceive the environment, the information from the different sensors are often integrated or fused. In this article, we propose to perform the sensor fusion and registration of the LIDAR and stereo camera using the particle swarm optimization algorithm, without the aid of any external calibration objects. The proposed algorithm automatically calibrates the sensors and registers the LIDAR range image with the stereo depth image. The registered LIDAR range image functions as the disparity map for the stereo disparity estimation and results in an effective sensor fusion mechanism. Additionally, we perform the image denoising using the modified non-local means filter on the input image during the stereo disparity estimation to improve the robustness, especially at night time. To evaluate our proposed algorithm, the calibration and registration algorithm is compared with baseline algorithms on multiple datasets acquired with varying illuminations. Compared to the baseline algorithms, we show that our proposed algorithm demonstrates better accuracy. We also demonstrate that integrating the LIDAR range image within the stereo’s disparity estimation results in an improved disparity map with significant reduction in the computational complexity.
关键词： stereo camera,LIDAR,sensor fusion
更新于2025-09-23 15:23:52
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Segmentation of Sloped Roofs from Airborne LiDAR Point Clouds Using Ridge-Based Hierarchical Decomposition
摘要： This paper presents a new approach for roof facet segmentation based on ridge detection and hierarchical decomposition along ridges. The proposed approach exploits the fact that every roof can be composed of a set of gabled roofs and single facets which are separated by the gabled roofs. In this work, firstly, building footprints stored in OpenStreetMap are used to extract 3D points on roofs. Then, roofs are segmented into roof facets. The algorithm starts with detecting roof ridges using RANSAC since they are parallel to the horizon and situated on the top of the roof. The roof ridges are utilized to indicate the location and direction of the gabled roof. Thus, points on the two roof facets along a roof ridge can be identified based on their connectivity and coplanarity. The results of the segmentation benefit the further process of roof reconstruction because many parameters, including the position, angle and size of the gabled roof can be calculated and used as priori knowledge for the model-driven approach, and topologies among the point segments are made known for the data-driven approach. The algorithm has been validated in the test sites of two towns next to Bavaria Forest national park. The experimental results show that building roofs can be segmented with both high correctness and completeness simultaneously.
关键词： OpenStreetMap,building roof,LiDAR,segmentation
更新于2025-09-23 15:23:52
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Ship-borne aerosol profiling with lidar over the Atlantic Ocean: From pure marine conditions to complex dust-smoke mixtures
摘要： The multiwavelength Raman lidar PollyXT has been regularly operated aboard the research vessel Polarstern on expeditions across the Atlantic Ocean from North to South and vice versa. The lidar measurements of the Polarstern cruises PS95 from Bremerhaven to Cape Town (November 2015) and PS98 from Punta Arenas to Bremerhaven (April/May 2016) are presented and analysed in detail. The latest setup of PollyXT allows improved coverage of the marine boundary layer (MBL) due to an additional near-range receiver. Three case studies provide an overview of the detected aerosol over the Atlantic Ocean. In the first case, marine conditions were observed near South Africa on the autumn cruise PS95. Values of optical properties (depolarisation ratios close to zero, lidar ratios of 23 sr at 355 nm and 532 nm) within the MBL indicate pure marine aerosol. A layer of dried marine aerosol, indicated by an increase of the particle depolarisation ratio to about 10% at both wavelengths and thus confirming the non-sphericity of these particles, could be detected on the top of the MBL. On the same cruise, an almost pure Saharan dust plume was observed near the Canary Islands, presented in the second case. The third case deals with several layers of Saharan dust partly mixed with biomass-burning smoke measured on PS98 near the Cape Verde Islands. While the MBL was partly mixed with dust in the pure Saharan dust case, an almost marine MBL was observed in the third case. A statistical analysis showed latitudinal differences in the optical properties within the MBL, caused by the down-mixing of dust in the tropics and anthropogenic influences in the northern latitudes whereas the optical properties of the MBL in the southern hemisphere correlate with typical marine values. The particle depolarisation ratio of dried marine layers ranged between 4–9%. Night measurements from PS95 and PS98 were used to illustrate the potential of aerosol classification using lidar ratio, particle depolarisation ratio and ?ngstr?m exponent. Lidar ratio and particle depolarisation ratio have been found to be the main indicator for the particle type, whereas the ?ngstr?m exponent is rather variable.
关键词： PollyXT,Atlantic Ocean,depolarisation,marine boundary layer,Polarstern,lidar,aerosol
更新于2025-09-23 15:23:52
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Performance Analysis of Flash LiDAR Based TRN Using Different Correlation Functions
摘要： This paper compares and analyzes a performance of template matching based terrain referenced navigation using correlation functions according to different error types and correlation functions. Conventional batch processing TRN generally utilizes the radar altimeter and adopts mean square difference, mean absolute difference, and normalized cross correlation for matching a batch profile with terrain database. If a flash LiDAR is utilized instead of the radar, it is possible to build a profile in one-shot. A point cloud of the flash LiDAR can be transformed into 2D profile, unlike a vector profile obtained from batch processing. Therefore, using the flash LiDAR we can apply new correlation functions such as image Euclidean distance and image normalized cross correlation which have been used in computer vision field. The simulation result shows that specific correlation functions are suitable for different types of errors.
关键词： Flash LiDAR,Template matching,TRN
更新于2025-09-23 15:23:52
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[IEEE 2018 15th Conference on Computer and Robot Vision (CRV) - Toronto, ON, Canada (2018.5.8-2018.5.10)] 2018 15th Conference on Computer and Robot Vision (CRV) - Learning a Bias Correction for Lidar-Only Motion Estimation
摘要： This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. Our technique is evaluated on over 50 km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.
关键词： Lidar Odometry,Gaussian Process,Motion Estimation,Bias Correction
更新于2025-09-23 15:23:52