Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars

Abstract

<span lang="EN-US">In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.</span>

An overview of emerging trends in robotics and automation

“…Additionally, AI and ML are helping to make automated vehicles and smart homes more widespread. It is clear that AI and ML are here to stay, as evidenced by the fact that there is a growing demand for AI professionals and that research and development in the field is expanding [10], [21]- [25].…”

“…Several major technology companies and automobile manufacturers are making significant investments in the development of autonomous vehicles, which is leading to improvements in ride-sharing services and solutions for last-mile delivery. The way in which we carry both people and things is going to be fundamentally altered by autonomous cars as regulations continue to develop to accommodate new technology [3], [4], [25], [37], [40], [41].…”

Deep neural network for lateral control of self-driving cars in urban environment

“…Together, they are actively pushing forward the development of next-generation autonomous vehicle solutions. The integration of artificial intelligence [2], deep learning [3], and sensor technology [4] has enabled significant improvements in the safety, reliability, and performance of autonomous cars [5]. Moreover, significant advancements in machine learning have allowed innovative approaches to autonomous vehicles using deep neural networks [6], [7].…”

Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach

“…GNSSs were used in various applications such as locating potential forest fires [1]; space surveillance and tracking system [2]; observation of coastal tides [3]; cadastral surveying [4] and localization and navigation system [5]. In addition to GNSS, simultaneous localization and mapping systems (SLAM) [6]- [8] and indoor localization and navigation systems [9]- [10] were developed for localization and navigation systems.…”