3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

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

Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots.Comment: Accepted in the 49th Annual Conference of the IEEE Industrial Electronics Society [IECON2023

Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.Comment: 19 page

Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization

In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets. Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. In this paper, we investigate this problem from the data perspective instead of detector architecture design. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges without modifying the detector architecture and without data augmentation. We propose a model-free point cloud density adjustment pre-processing mechanism that uses iterative MCMC optimization to estimate optimal parameters for altering the point density at different distance ranges. We conduct experiments using four state-of-the-art LiDAR 3D object detectors on two public LiDAR datasets, namely Waymo and ONCE. Our results demonstrate that our range-based point cloud density manipulation technique can improve the performance of the existing detectors, which in turn could potentially inspire future detector designs

LiDAR based multi-sensor fusion for localization, mapping, and tracking

Viimeisen vuosikymmenen aikana täysin itseohjautuvien ajoneuvojen kehitys on herättänyt laajaa kiinnostusta niin teollisuudessa kuin tiedemaailmassakin, mikä on merkittävästi edistänyt tilannetietoisuuden ja anturiteknologian kehitystä. Erityisesti LiDAR-anturit ovat nousseet keskeiseen rooliin monissa havainnointijärjestelmissä niiden tarjoaman pitkän kantaman havaintokyvyn, tarkan 3D-etäisyystiedon ja luotettavan suorituskyvyn ansiosta. LiDAR-teknologian kehittyminen on mahdollistanut entistä luotettavampien ja kustannustehokkaampien antureiden käytön, mikä puolestaan on osoittanut suurta potentiaalia parantaa laajasti käytettyjen kuluttajatuotteiden tilannetietoisuutta. Uusien LiDAR-antureiden hyödyntäminen tarjoaa tutkijoille monipuolisen valikoiman tehokkaita työkaluja, joiden avulla voidaan ratkaista paikannuksen, kartoituksen ja seurannan haasteita nykyisissä havaintojärjestelmissä. Tässä väitöskirjassa tutkitaan LiDAR-pohjaisia sensorifuusioalgoritmeja. Tutkimuksen pääpaino on tiheässä kartoituksessa ja globaalissa paikan-nuksessa erilaisten LiDAR-anturien avulla. Tutkimuksessa luodaan kattava tietokanta uusien LiDAR-, IMU- ja kamera-antureiden tuottamasta datasta. Tietokanta on välttämätön kehittyneiden anturifuusioalgoritmien ja yleiskäyttöisten paikannus- ja kartoitusalgoritmien kehittämiseksi. Tämän lisäksi väitöskirjassa esitellään innovatiivisia menetelmiä globaaliin paikannukseen erilaisissa ympäristöissä. Esitellyt menetelmät kartoituksen tarkkuuden ja tilannetietoisuuden parantamiseksi ovat muun muassa modulaarinen monen LiDAR-anturin odometria ja kartoitus, toimintavarma multimodaalinen LiDAR-inertiamittau-sjärjestelmä ja tiheä kartoituskehys. Tutkimus integroi myös kiinteät LiDAR -anturit kamerapohjaisiin syväoppimismenetelmiin kohteiden seurantaa varten parantaen kartoituksen tarkkuutta dynaamisissa ympäristöissä. Näiden edistysaskeleiden avulla autonomisten järjestelmien luotettavuutta ja tehokkuutta voidaan merkittävästi parantaa todellisissa käyttöympäristöissä. Väitöskirja alkaa esittelemällä innovatiiviset anturit ja tiedonkeruualustan. Tämän jälkeen esitellään avoin tietokanta, jonka avulla voidaan arvioida kehittyneitä paikannus- ja kartoitusalgoritmeja hyödyntäen ainutlaatuista perustotuuden kehittämismenetelmää. Työssä käsitellään myös kahta haastavaa paikannusympäristöä: metsä- ja kaupunkiympäristöä. Lisäksi tarkastellaan kohteen seurantatehtäviä sekä kameraettä LiDAR-tekniikoilla ihmisten ja pienten droonien seurannassa. ---------------------- The development of fully autonomous driving vehicles has become a key focus for both industry and academia over the past decade, fostering significant progress in situational awareness abilities and sensor technology. Among various types of sensors, the LiDAR sensor has emerged as a pivotal component in many perception systems due to its long-range detection capabilities, precise 3D range information, and reliable performance in diverse environments. With advancements in LiDAR technology, more reliable and cost-effective sensors have shown great potential for improving situational awareness abilities in widely used consumer products. By leveraging these novel LiDAR sensors, researchers now have a diverse set of powerful tools to effectively tackle the persistent challenges in localization, mapping, and tracking within existing perception systems. This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, with a primary focus on dense mapping and global localization using diverse LiDAR sensors. The research involves the integration of novel LiDARs, IMU, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced. These methodologies include a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios. The thesis commences with an introduction to innovative sensors and a data collection platform. It proceeds by presenting an open-source dataset designed for the evaluation of advanced SLAM algorithms, utilizing a unique ground-truth generation method. Subsequently, the study tackles two localization challenges in forest and urban environments. Furthermore, it highlights the MM-LOAM dense mapping framework. Additionally, the research explores object-tracking tasks, employing both camera and LiDAR technologies for human and micro UAV tracking

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems