## 2 Robot Environment Perception for Navigation
### Vision and Ranging Sensors
They offer more reliable performance in indoor and some outdoor scenarios. However, they require high computation capability. Table 1 shows the main depth measurement methods that are used in RGB-D sensors. Event cameras are asynchronous sensors that use different visual information acquisition principles than the standard frame-based image sensors. This camera type samples light based on the changes in the scene dynamics (asynchronously measuring brightness per pixel). These cameras currently cost several thousands of dollars. Event cameras have advantages like very high temporal resolution, high dynamic range, low latency (in microseconds), and lower power consumption than standard cameras. However, this camera type is not suitable for the detection of static objects. The main burden is the requirement of new methods (algorithms and hardware, e.g., neuromorphic-based approaches or spiking neural networks) to process event camera outputs to acquire data and information because traditional image processing methods are not directly applicable [44]. Omnidirectional cameras are utilised in robotic applications where more information is needed about surrounding environments. These cameras provide a wider-angle view than conventional cameras, which typically have a limited field of view. Due to the lens configuration of omnidirectional cameras, the obtained images have distortions. Therefore, these cameras require different mathematical models, like the unified projection model, to correct image distortions. A summary of these different camera types and their advantages and disadvantages is shown in Table 2.  
Active Ranging Sensors  
Active sensors emit energy to the environment and measure the return signal from the environment. There are several types of active-ranging sensors, such as reflectivity, ultrasonic, laser rangefinder (e.g., LiDAR), optical triangulation (1D), and structured light (2D). The commonly used LiDAR sensor is an active-ranging sensor that shows improved performance compared to ultrasonic TOF sensors for perception in autonomous navigation systems [46]. LiDAR imaging is one of the most studied topics in the optoelectronics field. The TOF measurement principle is used by LiDAR sensors for obtaining depth measure \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configurations** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Monocular} & Compactness, low hardware requirements & No direct depth measurements \\ \hline \multirow{3}{*}{Stereo} & \multirow{3}{*}{Depth measurements, low occlusions} & Fails in featureless environments, CPU intensive, accuracy/range depends on camera quality \\ \hline \multirow{3}{*}{RGB-D} & Colour and depth information per pixel & Limited range, reflection problems on transparent, shiny, or very matte and absorbing objects \\ \hline \multirow{3}{*}{Event} & High temporal resolution, suitable for changing light intensities, low latency [45] & No direct depth information, costly, not suitable for static scenes, requires non-traditional algorithms \\ \hline \multirow{3}{*}{Omnidirectional} & \multirow{3}{*}{Wide angle view (alternative to rotating cameras)} & Lower resolution, needs special methods to compensate for image distortions \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Camera configurations.