- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Evaluation of calibration methods to construct a 3-D environmental map with good color projection using both camera images and laser scanning data
摘要： In mobile robot navigation, restoration of the environment around the robot in a 3-D map is necessary for self-location, route planning, and detecting surrounding obstacles. We construct the 3-D color environmental map using both camera images and laser scanner (LIDAR) point cloud data. In the map, the RGB values are provided to the LIDAR point cloud data by projecting the point cloud onto the simultaneously acquired image. The projection parameters can be determined by measuring the calibration boards using both the camera and the LIDAR. In this paper, we have constructed a method to evaluate the accuracy of projection applicable to any calibration methods. And, then, we found that the calibration points in the central position of an image are important to obtain good projection parameters and that additional points at side positions also can improve the accuracy of the projection.
关键词： 3-D mapping,SLAM,Calibration,Data fusion
更新于2025-09-23 15:21:01
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[IEEE 2018 International Conference on 3D Vision (3DV) - Verona (2018.9.5-2018.9.8)] 2018 International Conference on 3D Vision (3DV) - Fusion++: Volumetric Object-Level SLAM
摘要： We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally re?ned via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is re?ned over time and an existence probability to account for spurious instance predictions.
关键词： SLAM,object-level mapping,Mask-RCNN,3D reconstruction,RGB-D
更新于2025-09-23 15:21:01
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[IEEE SoutheastCon 2018 - St. Petersburg, FL (2018.4.19-2018.4.22)] SoutheastCon 2018 - RGBD-Sphere SLAM
摘要： This article proposes a SLAM algorithm referred to as RGBD-Sphere SLAM. The key innovation of this work is the prototypical system that demonstrates how formal models of 3D geometric shape and appearance can be transformed into generative classification models that detect and recognize these shapes. Object models are specified as shape programs in PSML; a custom-built procedural language for 3D object modeling. Classifiers for each PSML shape are created by simulating how instances of each shape manifest in real-world sensor data, e.g., color images and range images. The proposed RGBD-Sphere SLAM algorithm demonstrates a prototypical example of the PSML program specifies spherical 3D objects having diffuse surface albedos and distinct color appearances. A recognizer uses PSML models of each object’s geometry and appearance to detect instances of these objects within streaming RGBD sensor data. The detected model parameters are then integrated into an RGBD SLAM algorithm; hence the name RGBD-Sphere SLAM. This article describes the PSML programs, the spherical detection and recognition algorithms used, and describes the impact this approach has for improving the performance of RGBD SLAM approaches by incorporating detected objects as landmarks. This is the first example of a prototypical system that externalizes the geometric and appearance modeling to a programming language from which a recognizer is created, and marks an important step towards enabling users to “program” their problem space and allow computers to transform the formal object models, as expressed in PSML, into customized classifiers suited for specific sensor suites, e.g., color imagery and depth imagery.
关键词： object recognition,RGBD,SLAM,3D object modeling,PSML
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR) - Irkutsk, Russia (2019.8.4-2019.8.9)] 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR) - A Hierarchical Searching Approach for Laser SLAM Re-localization on Mobile Robot Platform
摘要： Simultaneous localization and mapping (SLAM) is an important issue for mobile robot system. Re-localization, as a part of close loop checking of SLAM, focuses on localize the position of a robot agent when it is restarted. Traditional localization methods couldn’t reach both a high accuracy and a little time cost. In this paper, we proposed a newly hierarchical searching approach for laser SLAM re-localization. We do hierarchically searching to match an input laser frame with the whole 2-D map, instead of comparing it with each frame in the map. By using such approach, we could accelerate re-localization speed, so the performance of SLAM is improved. Our SLAM system is well organized on a mobile robot platform. After ?eld test, our approach reaches a average precision (AP) of 90.6% on a 20m× 20m map area, and the time cost is averagely 30ms. The result shows our hierarchical searching approach is effective for re-localization problems on laser SLAM.
关键词： SLAM,laser SLAM,hierarchical searching,mobile robot,re-localization
更新于2025-09-23 15:19:57
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Under-canopy UAV laser scanning for accurate forest field measurements
摘要： Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m 32 m test sites that were characterized as sparse ( = 42 trees) and obstructed ( = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories.
关键词： Stem volume,Under-canopy flight,SLAM,Airborne laser scanning,Stem curve,UAV
更新于2025-09-23 15:19:57
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Accurate derivation of stem curve and volume using backpack mobile laser scanning
摘要： Forest inventories rely on field plots, the measurement of which is costly and time consuming by manual means. Thus, there is a need to automate plot-level field data collection. Mobile laser scanning has yet to be demonstrated for deriving stem curve and volume from standing trees with sufficient accuracy for supporting forest inventory needs. We tested a new approach based on pulse-based backpack mobile laser scanner (Riegl VUX-1HA) combined with in-house developed SLAM (Simultaneous Localization and Mapping), and a novel post-processing algorithm chain that allows one to extract stem curves from scan-line arcs corresponding to individual standing trees. The post-processing step included, among others, an algorithm for scan-line arc extraction, a stem inclination angle correction and an arc matching algorithm correcting for the drifts that are still present in the stem points after applying the SLAM algorithm. By using the stem curves defined by the detected arcs and tree heights provided by the pulse-based scanner, stem volume estimates for standing trees in easy (n = 40) and medium (n = 37) difficult boreal forest were calculated. In the easy and medium plots, 100% of pine and birch stems were correctly detected. The total RMSE of the extracted stem curves was 1.2 cm (5.1%) and 1.7 cm (6.7%) for the easy and medium plots, respectively. The RMSE were 1.8 m (8.7%) and 1.1 m (4.9%) for the estimated tree heights, and 9.7% and 10.9% for the stem volumes for the easy and medium plots, correspondingly. Thus, our processing chain provided stem volume estimates with a better accuracy than previous methods based on mobile laser scanning data. Importantly, the accuracy of stem volume estimation was comparable to that provided by terrestrial laser scanning approaches in similar forest conditions. To further demonstrate the performance of the proposed method, we compared our results against stem volumes calculated using the standard Finnish allometric volume model, and found that our method provided more accurate volume estimates for the two test sites. The findings are important steps towards future individual-tree-based airborne laser scanning inventories which currently lack cost-efficient and accurate field reference data collection techniques. The tree geometry defined by the stem curve is also an important input parameter for deriving quality-related information from trees. Forest management decision making will benefit from improvements to the efficiency and quality of individual tree reference information.
关键词： SLAM,Tree volume,Mobile laser scanning,Stem curve,Stem volume,Mobile
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Miyazaki, Japan (2018.10.7-2018.10.10)] 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Navigation Control Design of a Mobile Robot by Integrating Obstacle Avoidance and LiDAR SLAM
摘要： This paper presents a mobile robot navigation control system based on integration of laser SLAM localization and real-time obstacle avoidance control to provide personnel guidance for daily-life services. The LiDAR SLAM localization system is implemented in a ROS software architecture, in which Cartographer SLAM is adopted and the adaptive Monte Carlo localization is employed onboard the robot. An integrated guidance system is proposed in this paper to combine obstacle avoidance and SLAM so that the robot can move to the desired location without colliding with any unexpected obstacles. A safety-weight parameter is used to integrate goal seeking controller with the obstacle avoidance controller. The experimental results show that the robot can localize itself and navigate to the target location while avoiding obstacles on the path.
关键词： SLAM,Mobile robot,Obstacle avoidance,Guidance control
更新于2025-09-19 17:15:36
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An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud
摘要： 3D laser simultaneous localization and mapping (SLAM) technology is one of the most efficient methods to capture spatial information. However, the low-precision of 3D laser SLAM point cloud limits its application in many fields. In order to improve the precision of 3D laser SLAM point cloud, we presented an offline coarse-to-fine precision optimization algorithm. The point clouds are first segmented and registered at the local level. Then, a pose graph of point cloud segments is constructed using feature similarity and global registration. At last, all segments are aligned and merged into the final optimized result. In addition, a cycle based error edge elimination method is utilized to guarantee the consistency of the pose graph. The experimental results demonstrated that our algorithm achieved good performance both in our test datasets and the Cartographer public dataset. Compared with the reference data obtained by terrestrial laser scanning (TLS), the average point-to-point distance root mean square errors (RMSE) of point clouds generated by Google’s Cartographer and LOAM laser SLAM algorithms are reduced by 47.3% and 53.4% respectively after optimization in our datasets. And the average plane-to-plane distances of them are reduced by 50.9% and 52.1% respectively.
关键词： laser SLAM,point clouds,LiDAR,precision optimization,mobile mapping
更新于2025-09-19 17:13:59
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[IEEE 2018 4th IEEE International Conference on Emerging Electronics (ICEE) - Bengaluru, India (2018.12.17-2018.12.19)] 2018 4th IEEE International Conference on Emerging Electronics (ICEE) - A simple high-efficiency forward-pumped Raman fiber laser
摘要： This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters, which are time synchronized to the physical transmitter and static in their positions. Contrary to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously; hence, the approach does not require any prior information, such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, this paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.
关键词： CRLB,multipath,positioning,particle filter,SLAM,Channel-SLAM
更新于2025-09-16 10:30:52
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[IEEE OCEANS 2019 - Marseille - Marseille, France (2019.6.17-2019.6.20)] OCEANS 2019 - Marseille - Laser Stripe Bathymetry using Particle Filter SLAM
摘要： This paper presents a novel application of BPSLAM from Barkby et al [1], onto underwater laser mapping. BP-SLAM is a featureless bathymetry mapping algorithm that uses Distributed Particle Mapping and a Rao-Backwelized Particle Filter (RBPF) to handle both navigational estimates and seafloor bathymetry estimates. By weighting the particles in the RBPF with the most self-consistent map, the bathymetric maps and the robot navigation are corrected.
关键词： Underwater robots,Underwater reconstruction,Bathymetric SLAM
更新于2025-09-16 10:30:52