# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 1 Introduction  
Technology is constantly changing and evolving to make human life more convenient and comfortable, and automation will be a major trend in future evolutions of technology. In the area of automation, a major development trend in the next few years will be autonomous cars [1].  
Because autonomous driving is the future trend and Light Detection and Ranging (LiDAR) is the core of autonomous driving, one of the key themes in autonomous car research in LiDAR [2; 3; 4; 5]. [PERSON] considers that LiDAR is 360 degrees surround sensor while the camera is a single view frontal view sensor [2]. Therefore, a LiDAR sensor can detect objects that are located on the sides and back of the ego vehicle [2]. [PERSON] uses the results of 3D LiDAR measurements in the data association function, resulting in a notable increase in robustness to person-to-person and person-to-background occlusions [2]. Moreover, [PERSON] adopts LiDAR sensors to perform \"vehicle detection using probability hypothesis density filter [3].\" [PERSON] considers that LiDAR is robust against light intensities and widely used in vehicle detection [3]. LiDAR provides a large number of measurements and tracks the potential objects without any association information or cluster process [3]. In addition, [PERSON] explains that LiDAR measures the surrounding environment containing the position (x, y, z coordinates) and intensity information of the objects [4]. LiDAR addsangular resolution (horizontal azimuth and vertical) for better measurement accuracy compared to the camera and radar sensors by measuring range [4]. Therefore, LiDAR is a core sensor that is used for autonomous vehicles [4]. Furthermore, LiDAR is a very powerful sensor with wide scanning range that can capture the depth value, distance and contour information of objects precisely [5]. At the same time, LiDAR is not easily affected by environmental interference and provides fast and accurate measured results [5]. For these reasons, many developers use this for autonomous vehicles. LiDAR-scanned packet information can provide distance, angle, and reflectivity information, and such information can be reassembled to construct a point-cloud map [2; 3; 4; 5]. In decoding the LiDAR sensor packet, the 3D coordinates must be calculated and the COordinate Rotation DIgital Computer (CORDIC) algorithm must be used to solve the sine (SIN) and cosine (COS) values. However, the traditional CORDIC algorithm requires a large number of iterations and entails a long delay time. If accuracy of N bits is required, N iterations must be performed. Such an algorithm is not suitable for autonomous-driving circuits, which require high-speed processing. However, if we use the look-up table (LUT) scheme [6], the circuit area will become larger. Therefore, the number of CORDIC iterations and the area of the CORDIC circuit need to be reduced for LiDAR sensors. To solve the above problems, this paper proposes an efficient CORDIC iteration circuit design for LiDAR sensors to reconstruct a point-cloud map application to autonomous driving.  
This paper is composed as follows. Section 2 describes the related work on LiDAR sensors and CORDIC circuits. Section 3 presents the LiDAR sensing system, LiDAR packet decoding, 3D point-cloud map reconstruction and the CORDIC iterative reduction algorithm. Then the mobile LiDAR sensing circuit and chip design are addressed in Section 4. In Section 5, experimental results are presented, and conclusions are stated in Section 6.