# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 1 Introduction  
In the late years, lidar (an acronym of light detection and ranging) has progressed from a useful measurement technique suitable for studies of atmospheric aerosols and aerial mapping, towards a kind of new Holy Grail in optomechanical engineering and optoelectronics. World-class engineering teams are launching start-ups and receiving relevant investments, and companies previously established in the field are being acquired by large industrial corporations, mainly from the automotive industry, or receiving heavy investments from the venture capital sector. The fuel of all this activity is the lack of an adequate solution in all aspects for lidar imaging systems for automobile either because of performance, lack of components, industrialization or cost issues. This has resulted in one of the strongest market-pull cases of the late years in the optics and photonics industries [1], a story which has even appeared in business journals such as Forbes [2], where sizes of the market in the billion unit level are claimed for the future Level 5 (fully automated) self-driving car. Lidar has been a well-known measurement technique since the past century, with established publications and a dense bibliography corpus. Lidar stands on a simple working principle based on counting the time between events in magnitudes carried out by light, such as e.g., backscattered energy from a pulsed beam. From these time measurements, the speed of light in air is used tocompute distances or to perform mapping. Quite logically, this is referred to as the time-of-flight (TOF) principle. Remote sensing has been one of the paramount applications of the technology, either from ground-based stations (e.g., for aerosol monitoring) or as aerial or space-borne instrumentation, typically for Earth observation applications in different wavebands. A number of review papers [3], introductory texts [4] or comprehensive books [5; 6] have been made available on the topic across the years. Lidar had become already so relevant that a full industry related to remote sensing and mapping was developed. The relevance of the lidar industry is further shown by the existence of standards, including a dedicated data format and file extension for lidar mapping (.LAS, from laser) which has become an official standard for 3D point cloud data exchange beyond the sensor and the software which generates the point clouds. Despite its interest and the proximity of the topic, it is not the goal of this paper to deal with remote sensing or mapping lidars, even if they provide images. They are already well-established, blooming research fields on their own with long track records, optimized experimental methods and dedicated data processing algorithms, published in specialized journals [7] and conferences [8]. However, a very relevant number of applications on the ground benefit from the capability of a sensor to capture the complete 3D information around a vehicle. Thus, still using time-counting of different events in light, first research-based, then commercial 3D cameras started to become available, and rapidly found applications in audiovisual segmentation [9], RGB+depth fusion [10], 3D screens [11], people and object detection [12] and, of course, in human-machine interfaces [13], some of them reaching the general consumer market, being the best-known example the Microsoft Kinect [14]. These commercial units were in almost all cases amplitude-modulated cameras where the phase of the emitted light was compared with the received one in order to compare their phase difference to estimate distance. A niche was covered, as solutions were optimal for indoor applications without a strong solar background, and although the spatial resolution of the images was limited, and depth resolution was kept in the cm level, they were still useful for applications involving large-scale objects (like humans). In the meanwhile, a revolution incubated in the mobility industry in the shape of the self-driving car, which is expected to completely disrupt our mobility patterns.