| 1. |
- Almqvist, Håkan, 1984-, et al.
(författare)
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Improving Point-Cloud Accuracy from a Moving Platform in Field Operations
- 2013
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Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781467356411 - 9781467356435 ; , s. 733-738
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.
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| 2. |
- Ahtiainen, Juhana, et al.
(författare)
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Normal Distributions Transform Traversability Maps : LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
- 2017
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 34:3, s. 600-621
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Tidskriftsartikel (refereegranskat)abstract
- Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.
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| 3. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Autonomous transport vehicles : where we are and what is missing
- 2015
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Ingår i: IEEE robotics & automation magazine. - 1070-9932 .- 1558-223X. ; 22:1, s. 64-75
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.
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| 4. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Drive the Drive : From Discrete Motion Plans to Smooth Drivable Trajectories
- 2014
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Ingår i: Robotics. - Basel, Switzerland : M D P I AG. - 2218-6581. ; 3:4, s. 400-416
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Tidskriftsartikel (refereegranskat)abstract
- Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation).
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| 5. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Fast, continuous state path smoothing to improve navigation accuracy
- 2015
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Ingår i: IEEE International Conference on Robotics and Automation (ICRA), 2015. - : IEEE Computer Society. - 9781479969234 ; , s. 662-669
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Konferensbidrag (refereegranskat)abstract
- Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.
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| 6. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
- 2017
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Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 - 9781538626832 ; , s. 1389-1395
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Konferensbidrag (refereegranskat)abstract
- Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.
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| 7. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Real time registration of RGB-D data using local visual features and 3D-NDT registration
- 2012
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Ingår i: Proc. of International Conference on Robotics and Automation (ICRA) Workshop on Semantic Perception, Mapping and Exploration (SPME). - : IEEE. - 9781467314039
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Konferensbidrag (refereegranskat)abstract
- Recent increased popularity of RGB-D capable sensors in robotics has resulted in a surge of related RGBD registration methods. This paper presents several RGB-D registration algorithms based on combinations between local visual feature and geometric registration. Fast and accurate transformation refinement is obtained by using a recently proposed geometric registration algorithm, based on the Three-Dimensional Normal Distributions Transform (3D-NDT). Results obtained on standard data sets have demonstrated mean translational errors on the order of 1 cm and rotational errors bellow 1 degree, at frame processing rates of about 15 Hz.
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| 8. |
- Bennetts, Victor Hernandez, 1980-, et al.
(författare)
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Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments
- 2014
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Ingår i: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 6362-6367
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Konferensbidrag (refereegranskat)abstract
- In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.
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| 9. |
- Canelhas, Daniel Ricão, et al.
(författare)
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A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry
- 2018
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Ingår i: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. ; , s. 6337-6343
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Konferensbidrag (refereegranskat)abstract
- Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories.
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| 10. |
- Canelhas, Daniel R., 1983-, et al.
(författare)
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Compressed Voxel-Based Mapping Using Unsupervised Learning
- 2017
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Ingår i: Robotics. - Basel, Switzerland : MDPI AG. - 2218-6581. ; 6:3
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Tidskriftsartikel (refereegranskat)abstract
- In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.
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