FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15~months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.Comment: 9 page

자율주행을 위한 정지 장애물 맵과 GMFT 융합 기반 이동 물체 탐지 및 추적

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 이경수.Based on the high accuracy of LiDAR sensor, detection and tracking of moving objects(DATMO) have been advanced as an important branch of perception for an autonomous vehicle. However, due to crowded road circumstances by various kind of vehicles and geographical features, it is necessary to reduce clustering fail case and decrease the computational burden. To overcome these difficulties, this paper proposed a novel approach by integrating DATMO and mapping algorithm. Since the DATMO and mapping are specialized to estimate moving object and static map respectively, these two algorithms can improve their estimation by using each others output. Whole perception algorithm is reconstructed using feedback loop structure includes DATMO and mapping algorithm. Moreover, mapping algorithm and DATMO are revised to innovative Bayesian rule-based Static Obstacle Map(SOM) and Geometric Model-Free Tracking(GMFT) to use each others output as the measurements of filtering process. The proposed study is evaluated via driving dataset collected by vehicles with RTK DGPS, RT-range and 2D LiDAR. Several typical clustering fail cases that had been observed in existing DATMO approach are reduced and code operation time over the whole perception process is decreased. Especially, estimation of moving vehicles state include position, velocity, and yaw angle show less error with references which are measured by RT-range.라이다 센서의 측정 정밀성을 기반으로 하여 DATMO, 즉 이동 물체 탐지 및 추적은 자율주행 인지 분야의 매우 중요한 주제로 발전되어 왔다. 그러나 다양한 종류의 차량에 의해 도로 상황이 복잡한 점 및 도로 특유의 복잡한 지형적 특성 때문에 클러스터링(Clustering)의 실패 사례가 종종 발생할 뿐만 아니라 인지 알고리즘의 계산 부담도 증가한다. 이러한 문제를 극복하기 위해 이 논문에서는 DATMO 알고리즘과 맵핑 알고리즘을 통합하여 새로운 접근법을 제시하였다. DATMO와 맵핑 알고리즘은 각각 이동 물체와 정지 물체의 상태를 추정하는데에 특화되어있기 때문에 두 알고리즘은 서로의 출력을 입력으로 사용하여 추정 성능을 향상시킬 수 있다. 전체 인지 알고리즘은 DATMO와 맵핑 알고리즘을 포함하는 피드백 루프 구조로 재구성된다. 또한 두 알고리즘은 각각 Geometric Model-Free Tracking(GMFT)과 베이지안 룰 기반의 혁신적인 Static Obstacle Map(SOM)으로 수정되어 서로의 출력을 필터링 프로세스의 측정값으로 사용한다. 이 연구에서 제시한 통합 인지 알고리즘은 RTK DGPS와 RT Range 장비, 그리고 2차원 LiDAR를 장착한 차량을 이용하여 수집한 데이터를 통해 성능을 평가하였다. 기존의 DATMO 연구에서 발생했던 몇 가지 일반적인 클러스터링 실패 사례가 감소하였고 전체 통합 인지 과정에 대한 알고리즘 작동 시간이 감소함을 확인하였다. 특히, 이동하는 물체의 위치, 속도, 방향을 추정한 결과는 RT Range 장비로 측정한 실제 값과 기존 방식 대비 더욱 적은 오차를 보여주었다.Chapter 1 Introduction 1 Chapter 2 Interaction of Mapping and DATMO 5 Chapter 3 Mapping – Static Obstacle Map 9 3.1 Prediction of SOM 11 3.2 Measurement update of SOM 14 Chapter 4 DATMO – Geometric Model-Free Tracking 16 4.1 Prediction of target state 18 4.2 Track management 19 4.3 Measurement update of target state 21 Chapter 5 Experimental Results 23 5.1 Vehicles and sensors configuration 24 5.2 Detection rate of moving object 27 5.3 State estimation accuracy of moving object 31 5.4 Code operation time 34 Chapter 6 Conclusion and Future Work 36 6.1 Conclusion 36 6.2 Future works 37 Bibliography 39 초 록 43Maste