# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 3 Principles of LRF and 3D Map Building  
### Principle of LRF Lane Scanning  
LRF measurement methods can be classified by time of flight measurement (TOF), triangular measurement, and phase-shift comparison measurement [27]. In this study, the phase-shift comparison measurement was used. Its advantages are a precise measurement at a very short distance and a low error range. This method involves electrical signals emitted by reflecting the phase and intensity of the laser, whereby the sensor measures the distance by comparing the intensities of the initially emitted signals and the returned signals reflected off the target using Equation (1).  
\[L=\frac{1}{2}\times\theta\div\left(\frac{f}{c}\times 2\pi\right) \tag{1}\]  
where \(L\) denotes the distance to the target; \(\theta\), the phase shift; \(f\), the laser frequency; and \(c\), the velocity of light [15].  
When a sensor is used in field environments to measure the phase shift, the intensity of the laser returning to the sensor reflected off the target object decreases because of external factors such as the distance of the target, angle, and optical reflection rate of the target [28]. Because diminished laser signals make the phase difference detection more difficult, the signals should be amplified before being interpreted; however, this process is prone to calculation errors.  
Figure 3 shows a schematic representation of the laser signal processing steps. The returning light reflected off the object is converted into electrical signals at the avalanche photodiode (APD) and amplified with an automatic gain control (AGC) amplifier. In this process, the closer the color of the object is to black, the less is the amount of energy returned, resulting in weak signals as compared to objects having other colors; thus, the signals are amplified at AGC and calculated to be located closer than other objects [29]. When this principle is applied to road scanning, a height difference in the asphalt lane is observed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Power** & 3.3 V \\ \cline{2-3}
**Controller** & **Clock** & 50 Mhz \\ \cline{2-3}
**(Mycortex-LM8962)** & **Memory** & 256 kB flash/64 kB SRAM \\ \cline{2-3}  & **Product Features** & 32-Bit RISC, GPT, SSI, UART, ADC, I2C, PWM, GPIO \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distance and angle measurements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Rated Voltage** & 12 V \\ \cline{2-3}
**Motor** & **Rated Torque** & 2.0 g-cm \\ \cline{2-3}
**(RB35 GM)** & **Rated Speed** & 102 rpm \\ \cline{2-3}  & **Output Power** & 3.14 W \\ \cline{2-3}  & **Deceleration** & 50:1 \\ \hline \multirow{4}{*}{**Motor Driver** (NT-S-DCDM1210)} & **Input Voltage Range** & 12\(\sim\)24V \\ \cline{2-3}  & **Maximum Continuous Current** & 5 A \\ \cline{2-3}  & **PWM Frequency** & 14.4 KHz \\ \cline{2-3}  & **Interface** & RS-232, I2C \\ \hline \hline \end{tabular}
\end{table}
Table 3: The specifications of motor and motor driver.  
Figure 4 shows the LRF-based road scan principle. As stated above, a black-colored object scanned with LRF is considerably amplified by AGC and the LRF-to-object distance appears to be shorter than it really is, whereas colored objects appear almost identical to their real distances [30; 31]. As a result, in a 3D road map built using the LRF data, asphalt seems to be higher than the lane.