Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

자율 주행을 위한 3D Point Cloud Data 기반 물체 탐지 및 분류 기법에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 서승우.A 3D LIDAR provides 3D surface information of objects with the highest position accuracy, among available sensors that can be utilized to develop perception algorithms for automated driving vehicles. In terms of automated driving, the accurate surface information gives the following benefits: 1) the accurate position information that is quite useful itself for collision avoidance is stably provided regardless of illumination condition, because the LIDAR is an active sensor. 2) the surface information can provide precise 3D shape-oriented features for object classification. Motivated by these characteristics, we propose three algorithms for a perception purpose of automated driving vehicles based on the 3D LIDAR in this dissertation. A very first procedure to utilize the 3D LIDAR as a perception sensor is segmentation that transform a stream of the LIDAR measurements into multiple point groups, where each point group indicate an individual object near the sensor. In chapter 2, a real-time and accurate segmentation is proposed. In particular, Gaussian Process regression is used to solve a problem called over-segmentation that increases False Positives by partitioning an object into multiple portions. The segmentation result can be utilized as input of another perception algorithm, such as object classification that is required for designing more human-likely driving strategies. For example, it is important to recognize pedestrians in urban driving environments because avoiding collisions with pedestrians are nearly a top priority. In chapter 3, we propose a pedestrian recognition algorithm based on a Deep Neural Network architecture that learns appearance variation. Another traffic participant that should be recognized with high-priority is a vehicle. Because various vehicle types of which appearances differ, such as a sedan, a bus, or a truck, are present on road, detection of the vehicles with similar performance regardless of the types is necessary. In chapter 4, we propose an algorithm that makes use of a common appearance of vehicles to solve the problem. To improve performance, a monocular camera is additionally employed, where the information from both sensors are integrated by a Dempster-Shafer Theory framework.Chapter 1 Introduction 1 1.1 Background and Motivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Real-time and Accurate Segmentation of 3D Point Clouds based on Gaussian Process Regression 3 1.2.2 Pedestrian Recognition Based on Appearance Variation Learning 4 1.2.3 Vehicle Recognition using a Common Appearance Captured by a 3D LIDAR and a Monocular Camera 5 Chapter 2 Real-time and Accurate Segmentation of 3D Point Clouds based on Gaussian Process Regression 6 2.1 Introduction 6 2.2 Related Work 10 2.3 Framework overview 15 2.4 Clustering of Non-ground Points 16 2.4.1 Graph Construction 17 2.4.2 Clustering of Points on Vertical Surface 17 2.4.3 Cluster Extension 21 2.5 Accuracy Enhancement 24 2.5.1 Approach to Handling Over-segmentation 26 2.5.2 Handling Over-segmentation with GP Regression 27 2.5.3 Learning Hyperparameters 31 2.6 Experiments 32 2.6.1 Experiment Environment 32 2.6.2 Evaluation Metrics 33 2.6.3 Processing Time 36 2.6.4 Accuracy on Various Driving Environments 37 2.6.5 Impact on Tracking 46 2.7 Conclusion 48 Chapter 3 Pedestrian recognition based on appearance variation learning 50 3.1 Introduction 50 3.2 Related Work 53 3.3 Appearance Variation Learning 56 3.3.1 Primal Input Data for the Proposed Architecture 57 3.3.2 Learning Spatial Features from Appearance 57 3.3.3 Learning Appearance Variation 59 3.3.4 Classification 61 3.3.5 Data Augmentation 61 3.3.6 Implementation Detail 61 3.4 EXPERIMENTS 62 3.4.1 Experimental Environment 62 3.4.2 Experimental Results 65 3.5 CONCLUSIONS AND FUTURE WORKS 70 Chapter 4 Vehicle Recognition using a Common Appearance Captured by a 3D LIDAR and a Monocular Camera 72 4.1 Introduction 72 4.2 Related Work 75 4.3 Vehicle Recognition 77 4.3.1 Point Cloud Processing 78 4.3.2 Image Processing 80 4.3.3 Dempster-Shafer Theory (DST) for Information Fusion 82 4.4 Experiments 84 4.5 Conclusion 87 Chapter 5 Conclusion 89 Bibliography 91 국문초록 105Docto

Structural Health Monitoring using Unmanned Aerial Systems

The use of Structural Health Monitoring (SHM) techniques is paramount to the safety and longevity of the structures. Many fields use this approach to monitor the performance of a system through time to determine the proper time and funds associated with repair and replacement. The monitoring of these systems includes nondestructive testing techniques (NDT), sensors permanently installed on the structure, and can also rely heavily on visual inspection. Visual inspection is widely used due to the level of trust owners have in the inspection personnel, however it is time consuming, expensive, and relies heavily on the experience of the inspectors. It is for these reasons that rapid data acquisition platforms must be developed using remote sensing systems to collect, process, and display data to decision makers quickly to make well informed decisions based on quantitative data or provide information for further inspection with a contact technique for targeted inspection. The proposed multirotor Unmanned Aerial System (UAS) platform carries a multispectral imaging payload to collect data and serve as another tool in the SHM cycle. Several demonstrations were performed in a laboratory setting using UAS acquired imagery for identification and measurement of structures. Outdoor validation was completed using a simulated bridge deck and ground based setups on in service structures. Finally, static laboratory measurements were obtained using multispectral patterns to obtain multiscale deformation measurements that will be required for use on a UAS. The novel multiscale, multispectral image analysis using UAS acquired imagery demonstrates the value of the remote sensing system as a nondestructive testing platform and tool for SHM.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Adaptive compression for 3D laser data

This paper concerns the creation of efficient surface representations from laser point clouds created by a push broom laser system. We produce a continuous, implicit, non-parametric and non-stationary representation with an update time that is constant. This allows us to form predictions of the underlying workspace surfaces at arbitrary locations and densities. The algorithm places no restriction on the complexity of the surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use of Gaussian Process Regression a natural choice. We adopt a formulation which handles the typical non-functional relation between XY location and elevation allowing us to map arbitrary environments. Results are presented that use real and synthetic data to analyse the trade-off between compression rate and reconstruction error. We attain decimation factors in excess of two orders of magnitude without significant degradation in fidelity. © The Author(s) 2011

Adaptive compression for 3D laser data

This paper concerns the creation of efficient surface representations from laser point clouds created by a push broom laser system. We produce a continuous, implicit, non-parametric and non-stationary representation with an update time that is constant. This allows us to form predictions of the underlying workspace surfaces at arbitrary locations and densities. The algorithm places no restriction on the complexity of the surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use of Gaussian Process Regression a natural choice. We adopt a formulation which handles the typical non-functional relation between XY location and elevation allowing us to map arbitrary environments. Results are presented that use real and synthetic data to analyse the trade-off between compression rate and reconstruction error. We attain decimation factors in excess of two orders of magnitude without significant degradation in fidelity. © The Author(s) 2011

Adaptive compression for 3D laser data

This paper concerns the creation of efficient surface representations from laser point clouds created by a push broom laser system. We produce a continuous, implicit, non-parametric and non-stationary representation with an update time that is constant. This allows us to form predictions of the underlying workspace surfaces at arbitrary locations and densities. The algorithm places no restriction on the complexity of the surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use of Gaussian Process Regression a natural choice. We adopt a formulation which handles the typical non-functional relation between XY location and elevation allowing us to map arbitrary environments. Results are presented that use real and synthetic data to analyse the trade-off between compression rate and reconstruction error. We attain decimation factors in excess of two orders of magnitude without significant degradation in fidelity. © The Author(s) 2011