###### Abstract
This is particularly true when the intended application area of autonomous vehicles/robots includes off-road areas. This low performance mainly arises due to the weak external environment understanding of robots. In general, application areas such as disaster management, environment exploration, defence, mining and transportation are associated with complex and unstructured environments. Therefore, these fields are also driving further research on mobile robot scene understanding as it relates to the broader topic of autonomous robot navigation. The following challenges for the classical autonomous robot navigation pipeline remain for current robotic systems:  
* Perception;
* Localisation and mapping; and
* Scene understanding. In robot perception, robots sense environments using different sensors and extract actionable information via the sensor data. Perception plays an important role in the realisation of autonomous robots. For robots to perform effectively in unstructured outdoor environments in real time, it is essential that they possess accurate, reliable, and robust perception capabilities. To achieve these characteristics, in general, autonomous robots in complex scenarios are equipped with several sensor modalities (that can be exteroceptive or proprioceptive) [5; 6]. Different modalities such as sound, pressure, temperature, light, and contact have been used in robot environmental perception applications [7]. Sensor fusion (combining different sensor modalities) has been applied in many recent autonomous mobile robot/self-driving applications [8]. Multimodal sensor fusion brings the complementary properties of different sensors together to achieve better environment perception across a range of conditions [9]. Many recent deep learning-based sensor fusion methods have shown higher robustness in perception than conventional mono-sensor methodologies [6; 10]. Camera, Light Detection and Ranging (LiDAR), radar, ultrasonic, Global Navigation Satellite System (GNSS), inertial measurement unit (IMU) and odometry sensors are used in many mobile ground robot perception applications. The most frequently used robot vision-based sensor fusion methods combine camera images with LiDAR point clouds. Research into sensor fusion techniques remains an important component of achieving better sensing capabilities for unstructured outdoor environments. In the second stage of the autonomous robot navigation pipeline, the information retrieved from perception sensors is used for robot localisation and to map their external environments. Autonomous mobile robots require accurate and reliable environmental mappings and localisation at a sophisticated level based on the application context. The Simultaneous Localisation and Mapping (SLAM) approach is a commonly used technique in autonomous mobile robot systems to represent the robot positions and the map of their external environments when both the robot pose and environmental map are previously unknown. Many SLAM systems use LiDAR sensors and vision-based sensors such as Red-Green-Blue (RGB)/RGB-Depth (RGB-D) cameras that can retrieve visual or proximity information, and the fusion of these sensors has achieved better robustness than using either camera or LiDAR sensors alone [11]. LiDAR sensors use highly accurate Time-of-Flight laser range finding for the detection of objects. An example of a lightweight and low-cost semantic environment classification approach for indoor robot navigation is introduced in [12]. Semantic classification becomes challenging with the variation of the lighting, shadows, occlusions, texture and the number of unseen semantic classes. Dynamic agents present another problem, [13] has proposed a LiDAR-based concept to segment large areas with dynamic agents. It can use LiDAR range view representations and further categorise objects in the images into semantic classes. Several research works have incorporated the fusion of camera and LiDAR data for improving scene understanding of outdoor environments [14].