## 2 Robot Environment Perception for Navigation
### Vision and Ranging Sensors  
Camera sensors are usually incorporated in vision applications by researchers to retrieve environmental information for the use of mobile robots. However, LiDAR sensors have shown more reliability in low-light environmental conditions than cameras and produce highly accurate depth measurements. LiDAR sensors come with 2D or 3D mapping capability [36], and these sensors can generate high-fidelity point clouds of outdoor environments. However, these unstructured point clouds tend to become increasingly sparse as the sensor range is increased. * Vision-based Sensor Types  
Figure 1: Classical and end-to-end autonomous navigation approaches. Vision is crucial for a robot to navigate in unknown environments. Robots are equipped with vision-based sensors like cameras to understand environmental information. Cameras are generally cheaper than LiDAR sensors and are a type of passive sensor (although some depth cameras use active sensing). The monocular configuration is highly used in standard RGB cameras, for example, the GoPro Hero camera series. They are compact passive sensors that consume low power, approximately 5 watts, depending on resolution and mode settings. Monocular SLAM has been explored in the research literature due to its simple hardware design [32; 37]. However, the algorithms that are used for it are very complex because depth measurements cannot be directly retrieved from static monocular images. Monocular cameras also suffer from pose estimation problems [38]. The pose of the camera is obtained by referencing previous poses. Hence, errors in pose estimation propagate through the process, and this phenomenon is called scale drift [28; 29; 39]. Stereo cameras are inspired by human eyes and use two lenses and separate passive image sensors to obtain two perspectives of the same scenes. They have been used in indoor and outdoor SLAM applications. These camera types use the disparity of the two images to calculate the depth information. Stereo camera vision does not suffer from scale drift. Available popular stereo cameras are Bumblebee 2, Bumblebee XB3, Surveyor stereo vision system, Capella, Minoru 3D Webcam, Ensenso N10, ZED 2, and PCI nDepth vision system. Stereo camera power consumption is generally between 2 to 15 watts. The maximum range of stereo cameras varies from 5 to 40 m at different depth resolution values. The accuracy of these sensors varies from around a few millimetres to 5 cm at the maximum range [40]. The cost of these sensors varies from one hundred to several thousand Australian dollars. RGB-D camera sensors consist of monocular or stereo cameras coupled to infrared transmitters and receivers. Kinect camera from Microsoft is a relatively inexpensive RGB-D sensor that provides colour images and depth information of image pixels. The Kinect sensor is mainly used for indoor robot applications due to the saturation of infrared receivers in outdoor scenarios from sunlight [41]. The Kinect sensor has three major versions: Kinect 1, Kinect 2, and Azure Kinect (the latest version). Kinect 1 uses structured light for depth measurement, and the other models use Time-Of-Flight (TOF) as the depth measuring principle. The newest Kinect model, Azure Kinect, also generates substantial noise in outdoor bright light conditions with a practical measuring range below 1.5 m [42]. In general, RGB-D sensors utilise three depth measurement principles: structured light, TOF, and active infrared stereo techniques. The structured light RGB-D sensors underperform compared to TOF techniques in measuring the range of distant objects. The structured light technique is vulnerable to multi-device interference. The TOF methods suffer from multi-path interference and motion artefacts. The active infrared stereo principle has drawbacks due to common stereo matching problems such as occluded pixels and flying pixels near contour edges [43]. Active infrared stereo cameras are identified as an extension of passive stereo cameras.