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Self-driving cars are steadily becoming a reality by a growing number of driver assistance functions enabled by smart perception sensors. The light detection and ranging (lidar) sensor show great potential for perception tasks due to its precise distance measurements. In order to take advantage of the high precision of a vehicle-mounted lidar, its position relative the vehicle needs to be calibrated. This is known as extrinsic calibration. The aim of this thesis is to investigate how to perform the extrinsic calibration of a static vehicle-mounted lidar in a static environment. In addition, the aim has been to develop a tool for running and customizing calibration simulations. The simulation tool CARLA, with its Python application programming interface (API), was chosen and developed to perform lidar simulations in a created virtual factory environment. The chosen calibration method uses a single vehicle-mounted lidar, targeting three spherical targets, whose known centre points act as reference points for the calibration. From the lidar point cloud a calibration algorithm is applied to find the position of the lidar. The algorithm estimates the centre points of the spherical targets and finds the lidar position by aligning the estimated centre points with the reference centre points. The algorithm includes functions that preprocess, cluster, fit spheres and perform point-to-point iterative closest point (ICP). The calibration method showed promising results in terms of point alignment and lidar position estimations. From 1000 simulations of random lidar positions, the average root mean square error (RMSE) of the point alignment was 0.33 mm with a standard deviation of 0.091 mm. The average absolute error of lidar position estimations was for translation [1.0 mm, 0.40 mm, 2.0 mm] with standard deviation [0.20 mm, 0.29 mm, 0.58 mm], and for rotation [0.11°, 0.11°, 0.10°] with standard deviation [0.098°, 0.098°, 0.094°]. Results also showed that uncertainties in the f

Self-driving cars are steadily becoming a reality by a growing number of driver assistance functions enabled by smart perception sensors. The light detection and ranging (lidar) sensor show great potential for perception tasks due to its precise distance measurements. In order to take advantage of the high precision of a vehicle-mounted lidar, its position relative the vehicle needs to be calibrated. This is known as extrinsic calibration. The aim of this thesis is to investigate how to perform the extrinsic calibration of a static vehicle-mounted lidar in a static environment. In addition, the aim has been to develop a tool for running and customizing calibration simulations. The simulation tool CARLA, with its Python application programming interface (API), was chosen and developed to perform lidar simulations in a created virtual factory environment. The chosen calibration method uses a single vehicle-mounted lidar, targeting three spherical targets, whose known centre points act as reference points for the calibration. From the lidar point cloud a calibration algorithm is applied to find the position of the lidar. The algorithm estimates the centre points of the spherical targets and finds the lidar position by aligning the estimated centre points with the reference centre points. The algorithm includes functions that preprocess, cluster, fit spheres and perform point-to-point iterative closest point (ICP). The calibration method showed promising results in terms of point alignment and lidar position estimations. From 1000 simulations of random lidar positions, the average root mean square error (RMSE) of the point alignment was 0.33 mm with a standard deviation of 0.091 mm. The average absolute error of lidar position estimations was for translation [1.0 mm, 0.40 mm, 2.0 mm] with standard deviation [0.20 mm, 0.29 mm, 0.58 mm], and for rotation [0.11°, 0.11°, 0.10°] with standard deviation [0.098°, 0.098°, 0.094°]. Results also showed that uncertainties in the f

Mitochondrial derived peptides (MDPs) humanin (HN) and mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) are involved in cell survival, suppression of apoptosis and metabolism. Circulating levels of MDPs are altered in chronic diseases such as diabetes type 2 and chronic kidney disease. Whether acute resistance (RE) or endurance (EE) exercise modulates circulating levels of HN and MOTS-c in humans is unknown. Following familiarization, subjects were randomized to EE (n=10, 45 min cycling at 70% of estimated VO2max), RE (n=10, 4 sets x 7RM, leg press and knee extension), or control (CON, n=10). Skeletal muscle biopsies and blood samples were collected before and at 30 minutes and 3 hours following exercise. Plasma concentration of HN and MOTS-c, skeletal muscle MOTS-c as well as gene expression of exercise related genes were analyzed. Acute EE and RE promoted changes in skeletal muscle gene expression typically seen in response to each exercise modality (c-Myc, 45S pre-rRNA, PGC-1α-total and PGC-1α-ex1b). At rest, circulating levels of HN were positively correlated to MOTS-c levels and age. Plasma levels of MDPs were not correlated to fitness outcomes (VO2max, leg strength or muscle mitochondrial (mt) DNA copy number). Circulating levels of HN were significantly elevated by acute EE but not RE. MOTS-C levels showed a trend to increase after EE. These results indicate that plasma MDP levels are not related to fitness status but that acute EE increases circulating levels of MDPs, in particular HN., QC 20211011