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

Modeling and Optimization of Power Management and Li-ion Batteries Health for Hydraulic-Electric Hybrid Vehicle.

The main goal of this work is to develop a systematic methodology to improve the range of electric vehicle and protect the battery health. Several other objectives enable achieving the main goal, including modeling, and power management optimization of hydraulic electric hybrid system, and battery degradation investigation and optimization. In order to improve the electric vehicle range, the hydraulic hybridization of electric vehicle is proposed. Physics based models of hydraulic electric hybrid vehicle are developed and the performance is analyzed. A near optimal and vehicle implementable rule-based energy management strategy is developed for the hydraulic-electric hybrid vehicle. The all electric range is improved by 68.3% through hybridization and control optimization. To further improve the range, the battery health is identified to be the key issue. Electrochemistry-based battery models are developed to investigate the degradation of the graphite/LiMn_2 O_4 cell. Our degradation study shows that the capacity fade can be divided into three stages: acceleration stage (SEI growth on anode is dominant), stabilization stage (SEI growth slows down and cathode capacity fade continues), and saturation stage (cathode has poor capacity and becomes the limiting factor). Cathode LMO fracture is repeatedly observed and suspected to be one important degradation mechanism in the cathode. A single particle fracture model is developed to investigate capacity fade induced by cathode fracture. The study shows that fracture introduces a significant capacity loss. In a 5 um particle with fracture, the capacity loss can reach to 13.7%. The particle size is another key factor that affects the mass transportation in the particle. Larger particles lead to higher internal resistance for electron transportation; therefore, fracture-induced capacity fade is more severe than with particles of smaller size. Based on the degradation analysis, a general procedure is developed to optimize the battery health while fulfilling the energy and power requirements. In total, this dissertation provides a systematic way to improve the range of electric vehicle by hydraulic hybridization and battery optimal design. The methodologies developed in this dissertation can be used to provide guidance for development of strategies for hybrid propulsion and optimal design of the battery health.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108840/1/xklin_1.pd

Optimal Charging Of Li-Ion Batteries Based On An Electrolyte Enhanced Single Particle Model

Lithium ion batteries play important roles as energy storage solutions in electric vehicle, portable devices, and renewable energy systems. There are many issues facing lithium ion batteries. One of them is the long charging time due to the slow electrochemical dynamics. Fast charging is one of the most difficult techniques that affect the acceptance of the electric vehicles. This paper presents a single particle battery model for charging optimization. The single particle model is enhanced with electrolyte dynamics. An optimal charging problem is formulated based on the electrolyte enhanced single particle model. Safety constraints are identified and imposed on the optimal charging problem. Multiple techniques have been developed to reduce the computational load. The fast charging strategy is developed. The results show that the fast charging strategy includes multiple phases and is able to reduce the charge time significantly

Large displacement vertical translational actuator based on piezoelectric thin films

A novel vertical translational microactuator based on thin-film piezoelectric actuation is presented, using a set of four compound bend-up/bend-down unimorphs to produce translational motion of a moving platform or stage. The actuation material is a chemical-solution deposited lead–zirconate–titanate (PZT) thin film. Prototype designs have shown as much as 120 µm of static displacement, with 80–90 µm displacements being typical, using four 920 µm long by 70 µm legs. Analytical models are presented that accurately describe nonlinear behavior in both static and dynamic operation of prototype stages when the dependence of piezoelectric coefficients on voltage is known. Resonance of the system is observed at a frequency of 200 Hz. The large displacement and high bandwidth of the actuators at low-voltage and low-power levels should make them useful to a variety of optical applications, including endoscopic microscopy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85407/1/jmm10_7_075016.pd

Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from image sensors (cameras) instead of laser-based sensors (LiDARs). Previous studies (such as PSMNet-based point cloud generation) built the algorithm based on accuracy but failed to operate in real time as LiDAR. We propose an approach to use different depth estimators to obtain pseudo point clouds similar to LiDAR to achieve better performance. Moreover, the depth estimator has used stereo imagery data to achieve more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms other existing approaches on KITTI depth prediction while yielding point clouds significantly faster than other approaches as well. Additionally, the proposed approach is evaluated on the KITTI stereo benchmark, where it shows effectiveness in runtime

Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from image sensors (cameras) instead of laser-based sensors (LiDARs). Previous studies (such as PSMNet-based point cloud generation) built the algorithm based on accuracy but failed to operate in real time as LiDAR. We propose an approach to use different depth estimators to obtain pseudo point clouds similar to LiDAR to achieve better performance. Moreover, the depth estimator has used stereo imagery data to achieve more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms other existing approaches on KITTI depth prediction while yielding point clouds significantly faster than other approaches as well. Additionally, the proposed approach is evaluated on the KITTI stereo benchmark, where it shows effectiveness in runtime