[{"data":1,"prerenderedAt":346},["ShallowReactive",2],{"doc-/logbook-experiments/programming/pseudo-lidar":3},{"id":4,"title":5,"author":6,"body":7,"date":329,"description":330,"extension":331,"image":332,"location":333,"meta":334,"navigation":335,"path":336,"seo":337,"stem":338,"tags":339,"__hash__":345},"content/logbook-experiments/programming/pseudo-lidar.md","[EXPERIMENT] Pseudo 3D LiDAR Using a 2D RPLiDAR-A2M12","Elsya Bekti N.",{"type":8,"value":9,"toc":315},"minimark",[10,15,28,31,36,39,70,75,78,98,100,104,115,118,128,131,150,152,156,159,163,236,238,242,249,282,284,288,295,313],[11,12,14],"h1",{"id":13},"pseudo-3d-lidar-development-and-evaluation","Pseudo 3D LiDAR Development and Evaluation",[16,17,18,19,23,24,27],"p",{},"In autonomous navigation, 2D LiDAR systems have inherent limitations when detecting objects with vertical variation, such as dock structures or overhanging obstacles. This experiment documents the development of a ",[20,21,22],"strong",{},"Pseudo 3D LiDAR"," by combining an ",[20,25,26],{},"RPLiDAR-A2M12"," sensor with a mechanical tilting mechanism.",[29,30],"hr",{},[32,33,35],"h2",{"id":34},"_1-motion-mechanism-and-actuation","1. Motion Mechanism and Actuation",[16,37,38],{},"To obtain the third dimension ($Z$), the 2D LiDAR is mounted on a vertically actuated tilting platform.",[40,41,42,54,60],"ul",{},[43,44,45,48,49,53],"li",{},[20,46,47],{},"Motion Scheme:"," Continuous ",[50,51,52],"em",{},"oscillating"," motion.",[43,55,56,59],{},[20,57,58],{},"Actuator:"," PowerHD 180° servo motor.",[43,61,62,65,66,69],{},[20,63,64],{},"Design Rationale:"," This approach was selected for its mechanical simplicity during early prototyping, as opposed to ",[50,67,68],{},"continuous rotation"," systems that require a slip ring.",[71,72,74],"h3",{"id":73},"angle-synchronization","Angle Synchronization",[16,76,77],{},"Synchronization between LiDAR range data and the servo elevation angle is achieved through PWM calibration:",[40,79,80,86,92],{},[43,81,82,85],{},[20,83,84],{},"$0^{\\circ}$ (Default Angle):"," Defined at PWM 1500.",[43,87,88,91],{},[20,89,90],{},"Minimum Angle:"," Measured at PWM 2600.",[43,93,94,97],{},[20,95,96],{},"Maximum Angle:"," Measured at PWM 700.",[29,99],{},[32,101,103],{"id":102},"_2-coordinate-transformation-ros-laser-assembler","2. Coordinate Transformation (ROS Laser Assembler)",[16,105,106,107,110,111,114],{},"The RPLiDAR outputs 2D polar measurements ($d, \\theta$). To construct a 3D point cloud, these measurements must be transformed using the servo elevation angle ($\\phi$). This experiment employs the ",[20,108,109],{},"laser_assembler"," package under ",[20,112,113],{},"ROS Melodic",".",[16,116,117],{},"Mathematically, the conversion to 3D Cartesian coordinates follows:",[16,119,120,121,124,125,127],{},"$$x = d \\cdot \\cos(\\theta) \\cdot \\cos(\\phi)$$",[122,123],"br",{},"\n$$y = d \\cdot \\sin(\\theta) \\cdot \\cos(\\phi)$$",[122,126],{},"\n$$z = d \\cdot \\sin(\\phi)$$",[16,129,130],{},"(refresh browser if equation does not appear)",[16,132,133,134,138,139,142,143,146,147,114],{},"Within the ROS ecosystem, this process is automated via the ",[135,136,137],"code",{},"tf"," (Transform Tree), linking the vehicle’s ",[135,140,141],{},"base_link"," to the ",[135,144,145],{},"laser_frame"," through an intermediate ",[135,148,149],{},"servo_link",[29,151],{},[32,153,155],{"id":154},"_3-experimental-results-and-performance-metrics","3. Experimental Results and Performance Metrics",[16,157,158],{},"All experiments were conducted using an NVIDIA Jetson Nano for data processing and point cloud visualization.",[71,160,162],{"id":161},"table-1-technical-specifications-and-performance-of-the-pseudo-3d-lidar","Table 1. Technical Specifications and Performance of the Pseudo 3D LiDAR",[164,165,166,180],"table",{},[167,168,169],"thead",{},[170,171,172,177],"tr",{},[173,174,176],"th",{"align":175},"left","Parameter",[173,178,179],{"align":175},"Experimental Result",[181,182,183,194,206,216,226],"tbody",{},[170,184,185,191],{},[186,187,188],"td",{"align":175},[20,189,190],{},"Field of View (FOV)",[186,192,193],{"align":175},"Horizontal 360°, Vertical 180° (−90° to 90°)",[170,195,196,201],{},[186,197,198],{"align":175},[20,199,200],{},"Update Rate (Scan Cycle)",[186,202,203],{"align":175},[20,204,205],{},"7 seconds per frame",[170,207,208,213],{},[186,209,210],{"align":175},[20,211,212],{},"CPU Load (Jetson Nano)",[186,214,215],{"align":175},"Relatively low (light point cloud processing)",[170,217,218,223],{},[186,219,220],{"align":175},[20,221,222],{},"Primary Sensor",[186,224,225],{"align":175},"RPLiDAR-A2M12 (Triangulation-based)",[170,227,228,233],{},[186,229,230],{"align":175},[20,231,232],{},"Test Environment",[186,234,235],{"align":175},"Indoor & Outdoor",[29,237],{},[32,239,241],{"id":240},"_4-evaluation-and-implementation-challenges","4. Evaluation and Implementation Challenges",[16,243,244,245,248],{},"Although the system successfully produced a 3D representation of obstacles—particularly beneficial for detecting structures during ",[50,246,247],{},"docking"," maneuvers that are difficult to capture with pure 2D scans—several critical limitations were identified:",[250,251,252,262,276],"ol",{},[43,253,254,257,258,261],{},[20,255,256],{},"High Latency:"," A 7-second scan cycle is far too slow for dynamic ASV (",[50,259,260],{},"Autonomous Surface Vehicle",") navigation, significantly increasing collision risk at higher vessel speeds.",[43,263,264,267,268,271,272,275],{},[20,265,266],{},"Data Stability:"," Without IMU integration to compensate for ",[50,269,270],{},"pitch"," and ",[50,273,274],{},"roll"," induced by wave motion, the reconstructed point cloud exhibits noticeable distortion and drift.",[43,277,278,281],{},[20,279,280],{},"Sensor Limitations:"," The RPLiDAR-A2M12 relies on triangulation, whose performance degrades severely under direct sunlight, making it unsuitable for outdoor RoboBoat competition conditions.",[29,283],{},[32,285,287],{"id":286},"_5-conclusions-and-design-decision","5. Conclusions and Design Decision",[16,289,290,291,294],{},"This Pseudo 3D LiDAR experiment provides a valuable proof of concept, highlighting the importance of multi-dimensional ",[50,292,293],{},"spatial awareness"," in autonomous navigation. However, based on the evaluation results:",[40,296,297,303,310],{},[43,298,299,302],{},[20,300,301],{},"The system was not adopted"," for the main competition due to its insufficient update rate.",[43,304,305,306,309],{},"The team decided to await the deployment of ",[50,307,308],{},"native"," 3D LiDAR solutions (such as solid-state or multibeam LiDARs) offering significantly higher refresh rates.",[43,311,312],{},"Current development efforts have shifted toward vision-based sensing (YOLOv4-Tiny) to improve detection of objects that are poorly perceived by 2D LiDAR alone.",[29,314],{},{"title":316,"searchDepth":317,"depth":317,"links":318},"",2,[319,323,324,327,328],{"id":34,"depth":317,"text":35,"children":320},[321],{"id":73,"depth":322,"text":74},3,{"id":102,"depth":317,"text":103},{"id":154,"depth":317,"text":155,"children":325},[326],{"id":161,"depth":322,"text":162},{"id":240,"depth":317,"text":241},{"id":286,"depth":317,"text":287},"2 January 2026","Implementation of a tilting 2D LiDAR mechanism for 3D point cloud generation, including coordinate transformation analysis and performance evaluation for RoboBoat 2026.","md","/images/research/programming/lidar.webp",null,{},true,"/logbook-experiments/programming/pseudo-lidar",{"title":5,"description":330},"logbook-experiments/programming/pseudo-lidar",[340,341,113,342,343,344],"LiDAR","Pseudo 3D","Point Cloud","Robotics","Jetson Nano","MnCRrx_dtMkZhM3oX345QnjvSDj5U0sE9zwB99P7WU4",1768857524014]