4 research outputs found
Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications
In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system
Large Scale 3D Mapping of Indoor Environments Using a Handheld RGBD Camera
The goal of this research is to investigate the problem of reconstructing a 3D representation of an environment, of arbitrary size, using a handheld color and depth (RGBD) sensor. The focus of this dissertation is to examine four of the underlying subproblems to this system: camera tracking, loop closure, data storage, and integration. First, a system for 3D reconstruction of large indoor planar environments with data captured from an RGBD sensor mounted on a mobile robotic platform is presented. An algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner is also presented. This approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of the mobile robotic platform. Through several experiments in environments with varying sizes and construction it is shown that this method reduces rotational and translational drift significantly without performing any loop closing techniques. In addition the advantages and limitations of an octree data structure representation of a 3D environment is examined. Second, the problem of sensor tracking, specifically the use of the KinectFusion algorithm to align two subsequent point clouds generated by an RGBD sensor, is studied. A method to overcome a significant limitation of the Iterative Closest Point (ICP) algorithm used in KinectFusion is proposed, namely, its sole reliance upon geometric information. The proposed method uses both geometric and color information in a direct manner that uses all the data in order to accurately estimate camera pose. Data association is performed by computing a warp between the two color images associated with two RGBD point clouds using the Lucas-Kanade algorithm. A subsequent step then estimates the transformation between the point clouds using either a point-to-point or point-to-plane error metric. Scenarios in which each of these metrics fails are described, and a normal covariance test for automatically selecting between them is proposed. Together, Lucas-Kanade data association (LKDA) along with covariance testing enables robust camera tracking through areas of low geometrical features, while at the same time retaining accuracy in environments in which the existing ICP technique succeeds. Experimental results on several publicly available datasets demonstrate the improved performance both qualitatively and quantitatively. Third, the choice of state space in the context of performing loop closure is revisited. Although a relative state space has been discounted by previous authors, it is shown that such a state space is actually extremely powerful, able to achieve recognizable results after just one iteration. The power behind the technique is that changing the orientation of one node is able to affect other nodes. At the same time, the approach --- which is referred to as Pose Optimization using a Relative State Space (POReSS) --- is fast because, like the more popular incremental state space, the Jacobian never needs to be explicitly computed. Furthermore, it is shown that while POReSS is able to quickly compute a solution near the global optimum, it is not precise enough to perform the fine adjustments necessary to achieve acceptable results. As a result, a method to augment POReSS with a fast variant of Gauss-Seidel --- which is referred to as Graph-Seidel --- on a global state space to allow the solution to settle closer to the global minimum is proposed. Through a set of experiments, it is shown that this combination of POReSS and Graph-Seidel is not only faster but achieves a lower residual than other non-linear algebra techniques. Moreover, unlike the linear algebra-based techniques, it is shown that this approach scales to very large graphs. In addition to revisiting the idea of using a relative state space, the benefits of only optimizing the rotational components of a trajectory in order to perform loop closing is examined (rPOReSS). Finally, an incremental implementation of the rotational optimization is proposed (irPOReSS)
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Όλ¬Έμμ λ€μν ν¬κΈ°μ μμΉ λ³νλμ λμνκΈ° μν λ€μ€ λ μ΄μ΄ NDT μ ν© (ML-NDT, multi-layered NDT)μ νκ³λ₯Ό 극볡νκΈ° μνμ¬ ν€λ μ΄μ΄ NDT μ ν© (KL-NDT, key-layered NDT)μ μ μνμλ€. KL-NDTλ κ° ν΄μλμ μ
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립λ μ€μΌμΌμ μ μνκ³ μ€μΌμΌμ λ³ννλ©° μ ν©νλ λμ μ€μΌμΌ κΈ°λ° NDT μ ν© (DSF-NDT-D2D, dynamic scaling factor-based NDT-D2D)μ μ μνμλ€. λ§μ§λ§μΌλ‘, λ³Έ λ
Όλ¬Έμ μμ€ NDTμ μ§λκ° μ¦λμ μ ν©μ μ΄μ©ν μ£Όνκ³ μΆμ λ° μ§λ μμ± λ°©λ²μ μ μνμλ€.
μ΄ λ°©λ²μ λ‘λ΄μ νμ¬ ν¬μ¦μ λν μ΄κΈ°κ°μ μμ€ μ κ΅°μ μ μ©ν λ€ NDTλ‘ λ³ννμ¬ μ§λ μ NDTμ κ°λ₯ν ν μ μ¬ν NDTλ₯Ό μμ±νλ€. κ·Έ λ€μ λ‘λ΄ ν¬μ¦ λ° μμ€ NDTμ GC (Gaussian component)λ₯Ό κ³ λ €νμ¬ λΆλΆμ§λλ₯Ό μΆμΆνλ€. μ΄λ κ² μΆμΆν λΆλΆμ§λμ μμ€ NDTλ λ€μ€ λ μ΄μ΄ NDT μ ν©μ μννμ¬ μ νν μ£Όνκ³λ₯Ό μΆμ νκ³ , μΆμ ν¬μ¦λ‘ μμ€ μ κ΅°μ νμ λ° μ΄λ ν κΈ°μ‘΄ μ§λλ₯Ό κ°±μ νλ€. μ΄λ¬ν κ³Όμ μ ν΅ν΄ μ΄ λ°©λ²μ νμ¬ μ΅κ³ μ±λ₯μ κ°μ§ LOAM (lidar odometry and mapping)μ λΉνμ¬ λ λμ μ νλμ λ λΉ λ₯Έ μ²λ¦¬μλλ₯Ό 보μλ€.The robot is a self-operating device using its intelligence, and autonomous navigation is a critical form of intelligence for a robot. This dissertation focuses on localization and mapping using a 3D range sensor for autonomous navigation. The robot can collect spatial information from the environment using a range sensor. This information can be used to reconstruct the environment. Additionally, the robot can estimate pose variations by registering the source point set with the model. Given that the point set collected by the sensor is expanded in three dimensions and becomes dense, registration using the normal distribution transform (NDT) has emerged as an alternative to the most commonly used iterative closest point (ICP) method. NDT is a compact representation which describes using a set of GCs (GC) converted from a point set. Because the number of GCs is much smaller than the number of points, with regard to the computation time,
NDT outperforms ICP. However, the NDT has issues to be resolved, such as the discretization of the point set and the objective function.
This dissertation is divided into two parts: representation and registration. For the representation part, first we present the probabilistic NDT (PNDT) to deal with the destruction and degeneration problems caused by the small cell size and the sparse point set. PNDT assigns an uncertainty to each point sample to convert a point set with fewer than four points into a distribution. As a result, PNDT allows for more precise registration using small cells. Second, we present lattice adjustment and cell insertion methods to overlap cells to overcome the discreteness problem of the NDT. In the lattice adjustment method, a lattice is expressed as the distance between the cells and the side length of each cell. In the cell insertion method, simple, face-centered-cubic, and body-centered-cubic lattices are compared. Third, we present a means of regenerating the NDT for the target lattice. A single robot updates its poses using simultaneous localization and mapping (SLAM) and fuses the NDT at each pose to update its NDT map. Moreover, multiple robots share NDT maps built with inconsistent lattices and fuse the maps. Because the simple fusion of the NDT maps can change the centers, shapes, and normal vectors of GCs, the regeneration method subdivides the NDT into truncated GCs using the target lattice and regenerates the NDT.
For the registration part, first we present a hue-assisted NDT registration if the robot acquires color information corresponding to each point sample from a vision sensor. Each GC of the NDT has a distribution of the hue and uses the similarity of the hue distributions as the weight in the objective function. Second, we present a key-layered NDT registration (KL-NDT) method. The multi-layered NDT registration (ML-NDT) registers points to the NDT in multiple resolutions of lattices. However, the initial cell size and the number of layers are difficult to determine. KL-NDT determines the key layers in which the registration is performed based on the change of the number of activated points. Third, we present a method involving dynamic scaling factors of the covariance. This method scales the source NDT at zero initially to avoid a negative correlation between the likelihood and rotational alignment. It also scales the target NDT from the maximum scale to the minimum scale. Finally, we present a method of incremental registration of PNDTs which outperforms the state-of-the-art lidar odometry and mapping method.1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Point Set Registration . . . . . . . . . . . . . . . . . . . . . 7
1.3.2 Incremental Registration for Odometry Estimation . . . . . . 16
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.5 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Preliminaries 21
2.1 NDT Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 NDT Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 NDT Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4 Transformation Matrix and The Parameter Vector . . . . . . . . . . . 27
2.5 Cubic Cell and Lattice . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.7 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.8 Evaluation of Registration . . . . . . . . . . . . . . . . . . . . . . . 31
2.9 Benchmark Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Probabilistic NDT Representation 34
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Uncertainty of Point Based on Sensor Model . . . . . . . . . . . . . . 36
3.3 Probabilistic NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4 Generalization of NDT Registration Based on PNDT . . . . . . . . . 40
3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5.2 Evaluation of Representation . . . . . . . . . . . . . . . . . . 41
3.5.3 Evaluation of Registration . . . . . . . . . . . . . . . . . . . 46
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Interpolation for NDT Using Overlapped Regular Cells 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2 Lattice Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3 Crystalline NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4.1 Lattice Adjustment . . . . . . . . . . . . . . . . . . . . . . . 56
4.4.2 Performance of Crystalline NDT . . . . . . . . . . . . . . . . 60
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5 Regeneration of Normal Distributions Transform 65
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 Mathematical Preliminaries . . . . . . . . . . . . . . . . . . . . . . . 67
5.2.1 Trivariate Normal Distribution . . . . . . . . . . . . . . . . . 67
5.2.2 Truncated Trivariate Normal Distribution . . . . . . . . . . . 67
5.3 Regeneration of NDT . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.1 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.2 Subdivision of Gaussian Components . . . . . . . . . . . . . 70
5.3.3 Fusion of Gaussian Components . . . . . . . . . . . . . . . . 72
5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4.1 Evaluation Metrics for Representation . . . . . . . . . . . . . 73
5.4.2 Representation Performance of the Regenerated NDT . . . . . 75
5.4.3 Computation Performance of the Regeneration . . . . . . . . 82
5.4.4 Application of Map Fusion . . . . . . . . . . . . . . . . . . . 83
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6 Hue-Assisted Registration 91
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Preliminary of the HSV Model . . . . . . . . . . . . . . . . . . . . . 92
6.3 Colored Octree for Subdivision . . . . . . . . . . . . . . . . . . . . . 94
6.4 HA-NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.5.1 Evaluation of HA-NDT against nhue . . . . . . . . . . . . . . 97
6.5.2 Evaluation of NDT and HA-NDT . . . . . . . . . . . . . . . 98
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7 Key-Layered NDT Registration 103
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.2 Key-layered NDT-P2D . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.3.1 Evaluation of KL-NDT-P2D and ML-NDT-P2D . . . . . . . . 108
7.3.2 Evaluation of KL-NDT-D2D and ML-NDT-D2D . . . . . . . 111
7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
8 Scaled NDT and The Multi-scale Registration 113
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
8.2 Scaled NDT representation and L2 distance . . . . . . . . . . . . . . 114
8.3 NDT-D2D with dynamic scaling factors of covariances . . . . . . . . 116
8.4 Range of scaling factors . . . . . . . . . . . . . . . . . . . . . . . . . 120
8.5 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.5.1 Evaluation of the presented method without initial guess . . . 122
8.5.2 Application of odometry estimation . . . . . . . . . . . . . . 125
8.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
9 Scan-to-map Registration 129
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
9.2 Multi-layered PNDT . . . . . . . . . . . . . . . . . . . . . . . . . . 130
9.3 NDT Incremental Registration . . . . . . . . . . . . . . . . . . . . . 132
9.3.1 Initialization of PNDT-Map . . . . . . . . . . . . . . . . . . 133
9.3.2 Generation of Source ML-PNDT . . . . . . . . . . . . . . . . 134
9.3.3 Reconstruction of The Target ML-PNDT . . . . . . . . . . . 134
9.3.4 Pose Estimation Based on Multi-layered Registration . . . . . 135
9.3.5 Update of PNDT-Map . . . . . . . . . . . . . . . . . . . . . 136
9.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
10 Conclusions 142
Bibliography 145
μ΄λ‘ 159
κ°μ¬μ κΈ 162Docto