DeepSpatial: Intelligent Spatial Sensor to Perception of Things

This paper discusses a spatial sensor to identify and track objects in the environment. The sensor is composed of an RGB-D camera that provides point cloud and RGB images and an egomotion sensor able to identify its displacement in the environment. The proposed sensor also incorporates a data processing strategy developed by the authors to conferring to the sensor different skills. The adopted approach is based on four analysis steps: egomotive, lexical, syntax, and prediction analysis. As a result, the proposed sensor can identify objects in the environment, track these objects, calculate their direction, speed, and acceleration, and also predict their future positions. The on-line detector YOLO is used as a tool to identify objects, and its output is combined with the point cloud information to obtain the spatial location of each identified object. The sensor can operate with higher precision and a lower update rate, using YOLOv2, or with a higher update rate, and a smaller accuracy using YOLOv3-tiny. The object tracking, egomotion, and collision prediction skills are tested and validated using a mobile robot having a precise speed control. The presented results show that the proposed sensor (hardware + software) achieves a satisfactory accuracy and usage rate, powering its use to mobile robotic. This paper's contribution is developing an algorithm for identifying, tracking, and predicting the future position of objects embedded in a compact hardware. Thus, the contribution of this paper is to convert raw data from traditional sensors into useful information.info:eu-repo/semantics/publishedVersio

Deep Learning Localization for Self-driving Cars

Identifying the location of an autonomous car with the help of visual sensors can be a good alternative to traditional approaches like Global Positioning Systems (GPS) which are often inaccurate and absent due to insufficient network coverage. Recent research in deep learning has produced excellent results in different domains leading to the proposition of this thesis which uses deep learning to solve the problem of localization in smart cars with visual data. Deep Convolutional Neural Networks (CNNs) were used to train models on visual data corresponding to unique locations throughout a geographic location. In order to evaluate the performance of these models, multiple datasets were created from Google Street View as well as manually by driving a golf cart around the campus while collecting GPS tagged frames. The efficacy of the CNN models was also investigated across different weather/light conditions. Validation accuracies as high as 98% were obtained from some of these models, proving that this novel method has the potential to act as an alternative or aid to traditional GPS based localization methods for cars. The root mean square (RMS) precision of Google Maps is often between 2-10m. However, the precision required for the navigation of self-driving cars is between 2-10cm. Empirically, this precision has been achieved with the help of different error-correction systems on GPS feedback. The proposed method was able to achieve an approximate localization precision of 25 cm without the help of any external error correction system

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed