Difficulties in capturing unique structures in the outdoor environment hinders the map-based Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.

Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current state-of-the-art can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnel-like scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits Structure-from-Motion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fully-dense 3D scene using the least possible number of images in various challenging tunnel-like environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnel-like environments, even those which are inaccessible for humans.

As the sensors and actuators for building an autonomous vehicle are getting increasingly affordable, both industry and research can now leverage on autonomous vehicle platform for various purposes. This paper presents a study of realizing a class of autonomous vehicle called Autonomous Service Vehicle (ASV) in the context of urban road navigation. The goal is to apply autonomous vehicle technology for human service oriented tasks. The ASV design is made for general service vehicles and it is intended to be reusable in term of hardware and software infrastructure. Components of the ASV are presented and experimentation was conducted to validate and evaluate the ASV. Further directions and necessary customization are explained in order to adapt the ASV design for various projects.

With the development of high reliability sensors, autonomous vehicles (AV) operating in real urban environments have been made possible. However, there still remain many challenges in improving the robustness and accuracy of AV localization in urban environments. In this paper, we propose a group of optimization techniques to make reliable map-based localization possible, using sparse point clouds from low cost light detection and ranging (LIDAR) sensors and odometry sensors. The map based Monte Carlo Localization (MCL) makes use of vertical and intensity features in it's observation model along with Heuristic Resampling to achieve robust localization. The proposed Heuristic Resampling picks the best candidate particle for localization output and resamples the remaining particles based on a defined heuristic. In addition, the LIDAR Calibration and Motion Compensation methods further improve localization accuracy. Experiments have been carried out to validate the effectiveness of the proposed techniques.

Human robot interaction is increasingly becoming an important subject of research as robots become more pervasive in human spaces. In this paper, we describe generating navigation paths that are more socially acceptable. A human user first divides the operating environment into segments, and then connects all adjacent segments by defining topological paths. These paths form the preferred routes through which robots should navigate, injecting a form of social norm such as not using certain private areas as intermediate waypoints and maintaining a keep-left/right behavior in walkways. Given arbitrary start and destination points, we compute appropriate entry and exit locations into any of the fixed paths and move there by using existing agility based planning methods. Once the robot gets onto the prescribed paths, it stays on these fixed routes but is allowed to make slight deviations to avoid obstacles. Collectively, our navigation framework exploits the best of route based navigation and agile based motion planning. We empirically show that our navigation method produces efficient movement in a hospital ward and hotel that is also socially acceptable.

Vehicle following is one of the fundamental functions of an autonomous driving system. Detection and recognition of tail light signal is important to prevent an autonomous vehicle from rear-end collisions or accidents. Although sensors like acoustic sonar or commercialized Advanced Driving Assistance System (ADAS) products such as mobileye could be used for rear-end collision warning, a cost-effective approach is expected. In this paper, we have developed a novel two-stage approach to detect vehicles and recognize brake lights from a single image in real-time. Unlike previous approaches where pair taillight has to be extracted explicitly, we use vehicle rear appearance image instead. On a large database, “Brake Lights Patterns” (BLP) are learned by a multi-layer perception neural network. Given an image, the vehicles can be classified as “brake” or “normal” using the deep classifier. The vehicle can be detected quickly and robustly by combining multi-layer lidar (IBEO Lux fusion system) and a camera. Road segmentation and a novel vanishing point region of interest (ROI) determination method are explored to further speed up the detection and improve the robustness. The experimental results conducted on some real on-road videos have shown the robustness and efficiency of the proposed approach.

This paper proposes a map free lane following solution based on low-cost 2D laser scanners for Autonomous Service Vehicle to fill the gap between future driverless car and the lane keeping assistant. The applications of autonomous service vehicle include feeder bus in a local residential area, shuttle bus in a park or playground, sprinkler car, sweeper car, and transporter in airport or container terminal. As autonomous service vehicle is running only in a limited area and its speed is slow compared to normal vehicles, we can further simplify the problem regardless of the issues of road infrastructure detection/communication and V2I maps which prevent the popularization of driverless car, and to propose a unique map free solution. The features of our approach include: 1) an innovative configuration for two 2D laser scanners to detect the lane with sharp curve; 2) a fast and accurate lane detection algorithm based on 2D laser's raw date directly; 3) a reliable and smooth path planning based on local lane fitting and prediction; and 4) a self-built unique drive-by-wire system for electronic car. We successfully tested our vehicle with autonomous driving in the testing field. The experiments show that the vehicle's trajectory matched the planned path accurately.

This paper presented a controller for autonomous stair identification, climbing, descending for a tracked robot. The controller relies on stair features extracted from the perception system and a smooth feedback steering control based on fuzzy logic. Experimental results indicate that the proposed method offers robust performance without the need for an accurate attitude estimator nor a detailed model of the robot's physical and dynamics properties. Experimental results have verified that the controller is sufficient for stair climbing and descending task in urban environments. Hence, the potential to be easily implemented on any tracked vehicles that are capable to be manually driven in a stair.

This paper presents a novel method to determine the multi-dimensional workspace boundary of parallel manipulators using a modified Particle Swarm Optimization (MPSO) framework. A generic objective (fitness) function that gives optimum value at points where a manipulator reaches its limit is formulated. By finding these points using MPSO, the workspace boundary of a parallel manipulator can be constructed. To demonstrate the method, the mapping of the boundary of a Stewart platform workspace is presented. The generic objective function can be modified easily to include any number of parameters and constraints, and this allows the method to be adapted for solving the workspace boundary problem of other parallel manipulators.