A Machine Learning and Point Cloud Processing based Approach for Object Detection and Pose Estimation: Design, Implementation, and Validation

This thesis presents an automatic forklift approach for lifting and handling pallets. The project more specifically develops a solution for autonomous object detection and pose estimation by Machine Learning (ML), point cloud processing, and arithmetic calculations.The project is based on a real-life scenario identified together with the industrial partner Red Rock, which includes a forklift operation, where the machine is supposed to identify,lift, and handle pallets autonomously. A key to achieving this automation is to localize and classify the pallet as well as to estimate the Six Dimensional (6D) pose of the pallet, which include its (x, y, z) position and (pitch, roll, yaw) orientation. Positioned directly in front of the pallet, the pose estimation must be performed around the range of 2-meter distance and 0° to ±45° angle. A systematic solution consisting of two major phases, object detection, and pose estimation, is developed to achieve the project goal. For object detection, the You Only Look Once X (YOLOX)-S ML algorithm is selected and implemented. The algorithm is pre-trained on the COCO dataset. It is, after that transfer, learned on the Logistics Objects in Context (LOCO) dataset to be able to detect pallets in an industrial environment. To improve the detection inference, the algorithm is optimized with the Intel OpenVINO toolkit, resulting in improved inference latency by over 2.5 times on Central Processing Unit (CPU). The output of the YOLOX-S algorithm is a bounding box around the pallet, and a custom struct links object detection and poses estimation together. The pose estimation algorithm converts the Two Dimensional (2D) bounding box data into Three Dimensional (3D) vectors, in which only the relevant points in the point cloud are kept. In contrast, all irrelevant points are filtered out from the environment. A series of arithmetic calculations from the filtered point cloud are applied, including Random Sample Consensus (RANSAC) and vector operations, in which the prior calculates the largest vertical plane of the identified pallet. Based on the object detection output and the pose estimation calculations, a 3D vector and a 3D point resulting in the pallet’s pose is found. Several tests and experiments have been performed to evaluate and validate the developed solution. The tests are based on a developed ground truth setup consisting of an AprilTag marker which provides a robust and precise ground truth measurement. Results from the standstill experiment show that the algorithm can estimate the position within 0.3 and 7.5 millimeters for the x and y axes. Moreover, the z-axis managed to be kept within 1.6 and 28.6 millimeters. The pitch orientation was kept within 3.65° and 5.21°, while the yaw orientation managed to be within 0.86° and 2.64°. Overall standstill test results have evaluated the best and worst case, respectively, within 0° and 45° degrees

A Machine Learning and Point Cloud Processing based Approach for Object Detection and Pose Estimation: Design, Implementation, and Validation

This thesis presents an automatic forklift approach for lifting and handling pallets. The project more specifically develops a solution for autonomous object detection and pose es- timation by Machine Learning (ML), point cloud processing, and arithmetic calculations. The project is based on a real-life scenario identified together with the industrial partner Red Rock, which includes a forklift operation, where the machine is supposed to identify, lift, and handle pallets autonomously. A key to achieving this automation is to localize and classify the pallet as well as to estimate the Six Dimensional (6D) pose of the pallet, which include its (x, y, z) position and (pitch, roll, yaw) orientation. Positioned directly in front of the pallet, the pose estimation must be performed around the range of 2-meter distance and 0° to ±45° angle. A systematic solution consisting of two major phases, object detection, and pose estimation, is developed to achieve the project goal. For object detection, the You Only Look Once X (YOLOX)-S ML algorithm is selected and implemented. The algorithm is pre-trained on the COCO dataset. It is, after that transfer, learned on the Logistics Objects in Context (LOCO) dataset to be able to detect pallets in an industrial environment. To improve the detection inference, the algorithm is optimized with the Intel OpenVINO toolkit, resulting in improved inference latency by over 2.5 times on Central Processing Unit (CPU). The output of the YOLOX-S algorithm is a bounding box around the pallet, and a custom struct links object detection and poses estimation together. The pose estimation algorithm converts the Two Dimensional (2D) bounding box data into Three Dimensional (3D) vectors, in which only the relevant points in the point cloud are kept. In contrast, all irrelevant points are filtered out from the environment. A series of arithmetic calculations from the filtered point cloud are applied, including Random Sample Consensus (RANSAC) and vector operations, in which the prior calculates the largest vertical plane of the identified pallet. Based on the object detection output and the pose estimation calculations, a 3D vector and a 3D point resulting in the pallet’s pose is found. Several tests and experiments have been performed to evaluate and validate the developed solution. The tests are based on a developed ground truth setup consisting of an AprilTag marker which provides a robust and precise ground truth measurement. Results from the standstill experiment show that the algorithm can estimate the position within 0.3 and 7.5 millimeters for the x and y axes. Moreover, the z-axis managed to be kept within 1.6 and 28.6 millimeters. The pitch orientation was kept within 3.65° and 5.21°, while the yaw ori- entation managed to be within 0.86° and 2.64°. Overall standstill test results have evaluated the best and worst case, respectively, within 0° and 45° degrees

A prospective follow-up study of chest pain patients - with emphasis on patients with panic disorder.

The aim of this thesis was to investigate the long-term prognosis of chest pain patients referred to a cardiological outpatient clinic with special emphasis on the importance of panic disorder (PD). 199 patients previously included in a study of psychological factors in chest pain patients were reassessed after one year by a psychiatric examination (SCID I interview and self-report questionnaires) and after nine years by both psychiatric and cardiological (including bicycle exercise test) examinations. At the baseline investigation 38% suffered from PD and 16% suffered from coronary artery disease. About 80% of eligible patients participated in the follow-up investigations. They were assessed regarding persistent PD, psychiatric morbidity, psychological distress, health related quality of life (HRQOL), treatment of PD, chest pain symptoms, mortality and non-fatal cardiac events. In summary, patients with PD at baseline reported a poor outcome regarding psychiatric morbidity, psychological distress and HRQOL and only few (10-18%) received PD treatment. The majority of patients reported persistent chest pain but they had a favorable outcome in terms of mortality and cardiac events

What is the role of “degree of worry” and “coping appraisal” in intentions for protective behaviour? Testing components of the protective motivation theory.

Protection motivation theory (PMT) has become a leading theory to explain the cognitive processes for risk management. The PMT suggest that cognitive likelihood evaluations predict protective behaviour. Consequently, several studies have focused on the effect of cognition on predicting intentions for protective behaviour against floods. However, empirical findings show contrasting evidence for the link between cognitive risk perception and protective behaviour. Additionally, recent studies have shown that affect is a better predictor of protective behaviour against flooding. With this background, the first aim of the current study was to test part of the PMT model by studying if affect (degree of worry) could be a better predictor of protective behaviour, than cognitive likelihood evaluations. The second aim was to test if affect (degree of worry) act as a moderator of coping appraisal in predicting intentions for protective behaviour. This study presents the result of a survey with 293 respondents from a Norwegian population. Two hypothesis were tested using regression- and moderation analysis. The current study found affect as a significant predictor, but risk perception likelihood was not a significant predictor of intentions for protective behaviour, when including affect (degree of worry). In addition, no significant result of affect as a moderator of coping appraisal in predicting protective behaviour, was found. This suggest that affect is central in risk perception processes, and that predictors of intentions for protective behaviour have a complex relationship

A Machine Learning and Point Cloud Processing based Approach for Object Detection and Pose Estimation: Design, Implementation, and Validation

This thesis presents an automatic forklift approach for lifting and handling pallets. The project more specifically develops a solution for autonomous object detection and pose es- timation by Machine Learning (ML), point cloud processing, and arithmetic calculations. The project is based on a real-life scenario identified together with the industrial partner Red Rock, which includes a forklift operation, where the machine is supposed to identify, lift, and handle pallets autonomously. A key to achieving this automation is to localize and classify the pallet as well as to estimate the Six Dimensional (6D) pose of the pallet, which include its (x, y, z) position and (pitch, roll, yaw) orientation. Positioned directly in front of the pallet, the pose estimation must be performed around the range of 2-meter distance and 0° to ±45° angle. A systematic solution consisting of two major phases, object detection, and pose estimation, is developed to achieve the project goal. For object detection, the You Only Look Once X (YOLOX)-S ML algorithm is selected and implemented. The algorithm is pre-trained on the COCO dataset. It is, after that transfer, learned on the Logistics Objects in Context (LOCO) dataset to be able to detect pallets in an industrial environment. To improve the detection inference, the algorithm is optimized with the Intel OpenVINO toolkit, resulting in improved inference latency by over 2.5 times on Central Processing Unit (CPU). The output of the YOLOX-S algorithm is a bounding box around the pallet, and a custom struct links object detection and poses estimation together. The pose estimation algorithm converts the Two Dimensional (2D) bounding box data into Three Dimensional (3D) vectors, in which only the relevant points in the point cloud are kept. In contrast, all irrelevant points are filtered out from the environment. A series of arithmetic calculations from the filtered point cloud are applied, including Random Sample Consensus (RANSAC) and vector operations, in which the prior calculates the largest vertical plane of the identified pallet. Based on the object detection output and the pose estimation calculations, a 3D vector and a 3D point resulting in the pallet’s pose is found. Several tests and experiments have been performed to evaluate and validate the developed solution. The tests are based on a developed ground truth setup consisting of an AprilTag marker which provides a robust and precise ground truth measurement. Results from the standstill experiment show that the algorithm can estimate the position within 0.3 and 7.5 millimeters for the x and y axes. Moreover, the z-axis managed to be kept within 1.6 and 28.6 millimeters. The pitch orientation was kept within 3.65° and 5.21°, while the yaw ori- entation managed to be within 0.86° and 2.64°. Overall standstill test results have evaluated the best and worst case, respectively, within 0° and 45° degrees