Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance

This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation

Development of a probabilistic perception system for camera-lidar sensor fusion

La estimación de profundidad usando diferentes sensores es uno de los desafíos clave para dotar a las máquinas autónomas de sólidas capacidades de percepción robótica. Ha habido un avance sobresaliente en el desarrollo de técnicas de estimación de profundidad unimodales basadas en cámaras monoculares, debido a su alta resolución o sensores LiDAR, debido a los datos geométricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminación en el caso delas cámaras y la resolución limitada de los sensores LiDAR. La fusión de sensores se puede utilizar para combinar los méritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los métodos de fusión actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo así estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimación de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con múltiples potenciales de geometría y apariencia que representa a la perfección el problema de estimar mapas de profundidad densos a partir de datos de cámara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conjúgate Gradient Squared (CGS). El método propuesto se evalúa y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se evalúa cualitativamente el modelo, usando datos adquiridos por el autor de esté trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There has been an outstanding advance in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution or LiDAR sensors due to the precise geometric data they provide. However, each of them suffers from some inherent drawbacks like high sensitivity to changes in illumination conditions in the case of cameras and limited resolution for the LiDARs. Sensor fusion can be used to combine the merits and compensate the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They processes sensor data streams independently and combine the high level estimates obtained for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw sensor streams, thus obtaining depth estimates which are both dense and precise, and can be used as a unified multi-modal data source for higher level estimation problems. This work proposes a Conditional Random Field (CRF) model with multiple geometry and appearance potentials that seamlessly represents the problem of estimating dense depth maps from camera and LiDAR data. The model can be optimized efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed method was evaluated and compared with the state-of-the-art using the commonly used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using data acquired by the author of this work.MaestríaMagíster en Ingeniería de Desarrollo de Producto

Development of a probabilistic perception system for camera-lidar sensor fusion

La estimación de profundidad usando diferentes sensores es uno de los desafíos clave para dotar a las máquinas autónomas de sólidas capacidades de percepción robótica. Ha habido un avance sobresaliente en el desarrollo de técnicas de estimación de profundidad unimodales basadas en cámaras monoculares, debido a su alta resolución o sensores LiDAR, debido a los datos geométricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminación en el caso delas cámaras y la resolución limitada de los sensores LiDAR. La fusión de sensores se puede utilizar para combinar los méritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los métodos de fusión actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo así estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimación de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con múltiples potenciales de geometría y apariencia que representa a la perfección el problema de estimar mapas de profundidad densos a partir de datos de cámara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conjúgate Gradient Squared (CGS). El método propuesto se evalúa y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se evalúa cualitativamente el modelo, usando datos adquiridos por el autor de esté trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There has been an outstanding advance in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution or LiDAR sensors due to the precise geometric data they provide. However, each of them suffers from some inherent drawbacks like high sensitivity to changes in illumination conditions in the case of cameras and limited resolution for the LiDARs. Sensor fusion can be used to combine the merits and compensate the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They processes sensor data streams independently and combine the high level estimates obtained for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw sensor streams, thus obtaining depth estimates which are both dense and precise, and can be used as a unified multi-modal data source for higher level estimation problems. This work proposes a Conditional Random Field (CRF) model with multiple geometry and appearance potentials that seamlessly represents the problem of estimating dense depth maps from camera and LiDAR data. The model can be optimized efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed method was evaluated and compared with the state-of-the-art using the commonly used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using data acquired by the author of this work.MaestríaMagíster en Ingeniería de Desarrollo de Producto

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

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

3D Sensor Placement and Embedded Processing for People Detection in an Industrial Environment

Papers I, II and III are extracted from the dissertation and uploaded as separate documents to meet post-publication requirements for self-arciving of IEEE conference papers.At a time when autonomy is being introduced in more and more areas, computer vision plays a very important role. In an industrial environment, the ability to create a real-time virtual version of a volume of interest provides a broad range of possibilities, including safety-related systems such as vision based anti-collision and personnel tracking. In an offshore environment, where such systems are not common, the task is challenging due to rough weather and environmental conditions, but the result of introducing such safety systems could potentially be lifesaving, as personnel work close to heavy, huge, and often poorly instrumented moving machinery and equipment. This thesis presents research on important topics related to enabling computer vision systems in industrial and offshore environments, including a review of the most important technologies and methods. A prototype 3D sensor package is developed, consisting of different sensors and a powerful embedded computer. This, together with a novel, highly scalable point cloud compression and sensor fusion scheme allows to create a real-time 3D map of an industrial area. The question of where to place the sensor packages in an environment where occlusions are present is also investigated. The result is algorithms for automatic sensor placement optimisation, where the goal is to place sensors in such a way that maximises the volume of interest that is covered, with as few occluded zones as possible. The method also includes redundancy constraints where important sub-volumes can be defined to be viewed by more than one sensor. Lastly, a people detection scheme using a merged point cloud from six different sensor packages as input is developed. Using a combination of point cloud clustering, flattening and convolutional neural networks, the system successfully detects multiple people in an outdoor industrial environment, providing real-time 3D positions. The sensor packages and methods are tested and verified at the Industrial Robotics Lab at the University of Agder, and the people detection method is also tested in a relevant outdoor, industrial testing facility. The experiments and results are presented in the papers attached to this thesis.publishedVersio

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future