Gaussian Process Mapping of Uncertain Building Models with GMM as Prior

Mapping with uncertainty representation is required in many research domains, such as localization and sensor fusion. Although there are many uncertainty explorations in pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid the potential problems caused by the errors of maps and a lack of the uncertainty quantification, an adequate uncertainty measure for the maps is required. In this paper, uncertain building models with abstract map surface using Gaussian Process (GP) is proposed to measure the map uncertainty in a probabilistic way. To reduce the redundant computation for simple planar objects, extracted facets from a Gaussian Mixture Model (GMM) are combined with the implicit GP map while local GP-block techniques are used as well. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performances of other methods such like Octomap, Gaussian Process Occupancy Map (GPOM) and Bayersian Generalized Kernel Inference (BGKOctomap), our method has achieved higher Precision-Recall AUC for evaluated buildings

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view images and LiDAR point cloud. Then, we employ a set of learnable semantic embeddings to encode the semantic types of map elements and supervise them with semantic segmentation, to make their feature representation consistent with BEV features. After that, we feed map queries, composed of learnable semantic embeddings and coordinates of map elements, into a transformer decoder to perform cross-modality matching with BEV features. Finally, we adopt a robust histogram-based pose solver to estimate the optimal pose by searching exhaustively over candidate poses. We comprehensively validate the effectiveness of our method using both the nuScenes dataset and a newly collected dataset. The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin. Furthermore, our model has been extensively tested in a large fleet of autonomous vehicles under various challenging urban scenes.Comment: 8 page

도심도로에서 자율주행차량의 라이다 기반 강건한 위치 및 자세 추정

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 이경수.This paper presents a method for tackling erroneous odometry estimation results from LiDAR-based simultaneous localization and mapping (SLAM) techniques on complex urban roads. Most SLAM techniques estimate sensor odometry through a comparison between measurements from the current and the previous step. As such, a static environment is generally more advantageous for SLAM systems. However, urban environments contain a significant number of dynamic objects, the point clouds of which can noticeably hinder the performance of SLAM systems. As a countermeasure, this paper proposes a 3D LiDAR SLAM system based on static LiDAR point clouds for use in dynamic outdoor urban environments. The proposed method is primarily composed of two parts, moving object detection and pose estimation through 3D LiDAR SLAM. First, moving objects in the vicinity of the ego-vehicle are detected from a referred algorithm based on a geometric model-free approach (GMFA) and a static obstacle map (STOM). GMFA works in conjunction with STOM to estimate the state of moving objects in real-time. The bounding boxes occupied by these moving objects are utilized to remove points corresponding to dynamic objects in the raw LiDAR point clouds. The remaining static points are applied to LiDAR SLAM. The second part of the proposed method describes odometry estimation through referred LiDAR SLAM, LeGO-LOAM. The LeGO-LOAM, a feature-based LiDAR SLAM framework, converts LiDAR point clouds into range images, from which edge and planar points are extracted as features. The range images are further utilized in a preprocessing stage to improve the computation efficiency of the overall algorithm. Additionally, a 6-DOF transformation is utilized, the model equation of which can be obtained by setting a residual to be the distance between an extracted feature of the current step and the corresponding feature geometry of the previous step. The equation is optimized through the Levenberg-Marquardt method. Furthermore, GMFA and LeGO-LOAM operate in parallel to resolve computational delays associated with GMFA. Actual vehicle tests were conducted on urban roads through a test vehicle equipped with a 32-channel 3D LiDAR and a real-time kinematics GPS (RTK GPS). Validations results have shown the proposed method to significantly decrease estimation errors related to moving feature points while securing target output frequency.본 연구는 복잡한 도심 환경에서 라이다 기반 동시적 위치 추정 및 맵핑(Simultaneous localization and mapping, SLAM)의 이동량 추정 오류를 방지하는 방법론을 제안한다. 대부분의 SLAM은 이전 스텝과 현재 스텝의 센서 측정치를 비교하여 자차량의 이동량을 추정한다. 따라서 SLAM에는 정적인 환경이 필수적이다. 그러나 센서는 도심환경에서 동적인 물체에 쉽게 노출되고 동적 물체로부터 출력되는 라이다 점군들은 이동량 추정 성능을 저하시킬 수 있다. 이에, 본 연구는 동적인 도심환경에서 정적인 점군을 기반한 3차원 라이다 SLAM 시스템을 제안하였다. 제안된 방법론은 이동 물체 인지와 3차원 라이다 SLAM을 통한 위치 및 자세 추정으로 구성된다. 우선, 기하학적 모델 프리 접근법과 정지 장애물 맵의 상호 보완적인 관계에 기반한 참고된 알고리즘을 이용해 자차량 주변의 이동 물체의 동적 상태를 실시간으로 추정한다. 그 후, 추정된 이동 물체가 차지하는 경계선을 이용하여 동적 물체에 해당하는 점들을 기존 라이다 점군에서 제거하고, 결과로 얻은 정적인 라이다 점군은 라이다 SLAM에 입력된다. 다음으로, 제안된 방법론은 라이다 SLAM을 통해 자차량의 위치 및 자세를 추정한다. 이를 위해 본 연구는 라이다 SLAM의 프레임워크인 LeGO-LOAM을 채택하였다. 특징점 기반 SLAM인 LeGO-LOAM은 라이다 점군을 거리 기반 이미지로 변환시켜 특징점인 모서리 점과 평면 점을 추출한다. 또한 거리 기반 이미지를 사용한 전처리 과정을 통해 계산 효율을 높인다. 추출된 현재 스텝의 특징점과 이에 대응되는 이전 스텝의 특징점으로 이루어진 기하학적 구조와의 거리를 잔차로 설정하여 6 자유도 변환식에 대한 모델 방정식을 얻을 수 있다. 참고한 LeGO-LOAM은 해당 방정식을 Levenberg-Marquardt 방법을 통해 최적화를 수행한다. 또한, 본 연구는 참고된 인지 모듈의 처리 지연 문제를 보완하기 위해 이동 물체 인지 모듈과 LeGO-LOAM의 병렬 처리 구조를 고안하였다. 실험은 도심환경에서 32채널 3차원 라이다와 고정밀 GPS를 장착한 실험차량으로 진행되었다. 성능 검증 결과, 제안된 방법은 목표 출력 속도를 보장하면서 움직이는 특징점으로 인한 추정 오차를 유의미하게 줄일 수 있었다.Chapter 1. Introduction １ 1.1. Research Motivation １ 1.2. Previous Research ３ 1.2.1. Moving Object Detection ３ 1.2.2. SLAM ４ 1.3. Thesis Objective and Outline １３ Chapter 2. Methodology １５ 2.1. Moving Object Detection & Rejection １５ 2.1.1. Static Obstacle Map １５ 2.1.2. Geometric Model-Free Approach １８ 2.2. LiDAR SLAM ２２ 2.2.1. Segmentation ２２ 2.2.2. Feature Extraction ２３ 2.2.3. LiDAR Odometry and Mapping ２６ 2.2.4. LiDAR SLAM with Static Point Cloud ２８ Chapter 3. Experiments ３０ 3.1. Experimental Setup ３０ 3.2. Error Metrics ３２ 3.3. LiDAR SLAM using Static Point Cloud ３６ Chapter 4. Conclusion ４４ Bibliography ４５석

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

Improving Scan Registration Methods Using Secondary Point Data Channels

Autonomous vehicle technology has advanced significantly in recent years and these vehicles are poised to make major strides into everyday use. Autonomous vehicles have already entered military and commercial use, performing the dirty, dull, and dangerous tasks that humans do not want to, or cannot perform. With any complex autonomy task for a mobile robot, a method is required to map the environment and to localize within that environment. In unknown environments when the mapping and localization stages are performed simultaneously, this is known as Simultaneous Localization and Mapping (SLAM). One key technology used to solve the SLAM problem involves matching sensor data in the form of point clouds. Scan registration attempts to find the transformation between two point clouds, or scans, which results in the optimal overlap of the scan information. One of the major drawbacks of existing approaches is the over-reliance on geometric features and a well structured environment in order to perform the registration. When insufficient geometric features are present to constrain the optimization, this is known as geometric degeneracy, and can be a common problem in typically environments. The reliability of these methods is of vital importance in order to improve the robustness of autonomous vehicles operating in uncontrolled environments. This thesis presents methods to improve upon existing scan registration methods by incorporating secondary information into the registration process. In this work, three methods are presented: Ground Segmented Iterative Closest Point (GSICP), Color Clus- tered Normal Distribution Transform (CCNDT), and Multi Channel Generalized Iterative Closest Point (MCGICP). Each method provides a unique addition to the scan registration literature and has its own set of benefits, limitations, and uses. GSICP segments the ground plane from a 3D scan then compresses the scan into a 2D plane. The points are then classified as either ground-adjacent, or non-ground-adjacent. Using this classification, a class constrained ICP registration is performed where only points of the same class can be corresponded. This results in the method essentially creating simulated edges for the registration to align. GSICP improves accuracy and robustness in sparse unstructured environments such as forests or rolling hills. When compared to existing methods on the Ford Vision and Lidar Dataset, GSICP shows a tighter variance in error values as well as a significant improvement in overall error. This method is also shown to be highly computationally efficient, running registrations on a low power system twice as fast as GICP, the next most accurate method. However, it does require the input scans to have specific characteristics such as a defined ground plane and spatially separated objects in the environment. This method is ideally suited for outdoor sparse environments and was used with great success by the University of Waterloo’s entry in the NASA Sample Return Robot Challenge. CCNDT provides a more adaptable method that is widely applicable to many common environments. CCNDT uses point cloud data which has been colorized either from an RGBD camera or a joint LIDAR and camera system. The method begins by clustering the points in the scan based on color and then uses the clusters to generate colored Gaussian distributions. These distributions are then used to calculate a color weighted distribution to distribution cost between all pairs of distributions. Exhaustively matching all pairs of distributions creates a smooth, continuous cost function that can be optimized efficiently. Experimental validation of the CCNDT method on the Ford and Freiburg datasets has shown that the method can perform 3D scan registrations more efficiently, three times faster on average then existing methods, and is capable of accurately registering any scans which have sufficient color variation to enable color clustering. MCGICP is a generalized approach that is capable of performing robustly in almost any situation. MCGICP uses secondary point information, such as color, intensity, etc., to augment the GICP method. MCGICP calculates a spacial covariance at each point such that the covariance normal to the local surface is set to a small value, indicating a high confidence matching surfaces, and the covariance tangent to the surface is determined based on the secondary information distribution. Having the covariance represented in both the tangential and normal directions causes non-trivial cost terms to be present in all directions. Additionally, the correspondence of points between scans is modified to use a higher dimensional search space, which incorporates the secondary descriptor channels as well as the covariance information at each point and allows for more robust point correspondences to be determined. The registration process can therefore converge more quickly due to the incorporation of additional information. The MCGICP method is capable of performing highly accurate scan registrations in almost any environmental situation. The method is validated using a diverse set of data including the Ford and Freiburg datasets, as well as a challenging degenerate dataset. MCGICP is shown to improve accuracy and reliability on all three datasets. MCGICP is robust to most common degeneracies as it incorporates multiple channels of information in an integrated approach that is reliable even in the most challenging cases. The results presented in this work demonstrate clear improvements over the existing scan registration methods. This work shows that by incorporating secondary information into the scan registration problem, more robust and accurate solutions can be obtained. Each method presented has its own unique benefits, which are valuable for a specific set of applications and environments