## 6 Discussion
### Additional Considerations  
Bundle loads with uneven shapes or protruding elements pose a significant hurdle for autonomous forklifts. These features can prevent the forklift from securely grasping the load, leading to instability during transport. This instability increases the risk of the load shifting or falling, potentially causing damage to the goods, surrounding infrastructure and injuring nearby personnel. Furthermore, irregular shapes can disrupt the forklift's sensors, hindering its ability to accurately measure distances and navigate obstacles. Therefore, ensuring the safe handling of such loads is crucial. This necessitates advancements in sensor technology and more sophisticated algorithms to mitigate these risks and maintain the safety standards required for autonomous forklifts. Autonomous vehicles require perceptual systems to understand their surroundings, which can be categorized into 2D and 3D perception. In the context of cars, 2D and 3D perception rely on cameras and sensors to capture visual information like pictures and video streams. Algorithms analyze these data to identify objects, interpret traffic signals and recognize pedestrians or other vehicles. This allows the vehicle to perceive the environment and take appropriate actions. However, forklifts operate in complex 3D environments like warehouses with racks, merchandise and other obstacles. In such scenarios, 2D perception alone is insufficient for safe and efficient navigation. Therefore, forklifts often utilize depth-sensing technologies like LiDAR or depth cameras to acquire 3D data about their surroundings. This enables accurate perception of object sizes, shapesand positions in three-dimensional space. While computer vision applications for cars primarily address two-dimensional challenges, forklifts face the additional complexity of navigating in three dimensions, exemplified by the Bird's-Eye View (BEV) mapping paradigm used in automotive technology, which employs a 2D top-down perspective. In all scenarios, additional information enhances the vehicle's ability to make safe and efficient decisions. Whether the vehicle is a car or a forklift, more data enable a more comprehensive understanding of its surroundings and internal state, which is crucial for effective operation. Among the various sensors employed in autonomous forklifts, LiDAR and cameras stand out as the most prevalent due to their comprehensive capabilities in mapping, navigation and object detection. LiDAR's exceptional accuracy in generating 3D maps, coupled with cameras' versatility in visual recognition tasks, makes them indispensable components in the sensor arrays of these autonomous vehicles. Moreover, when considering sensor specifications, key factors include frame rate, which indicates the number of frames captured per second, often measured in frames per second (fps). Size refers to the physical dimensions of the sensor, encompassing overall size and pixel dimensions. Visibility pertains to the sensor's ability to capture data in different lighting conditions or environments. The field of view describes the extent of the scene captured by the sensor. Resolution denotes the level of detail in the captured images. Weather effects indicate the sensor's resilience in diverse weather conditions. Range signifies the distance over which the sensor can effectively operate. Depending on the type of sensor, these specifications can vary, ensuring optimal performance in specific applications. As mentioned in [143; 142], numerous leading manufacturers of autonomous forklifts commonly employ a diverse array of machine vision models, including convolutional neural networks, deep learning models, 3D vision and depth sensing models, semantic segmentation models, instance segmentation models, reinforcement learning models and optical character recognition (OCR) models. CNNs, such as YOLO, SSD and Faster R-CNN, are widely adopted for image recognition and object detection due to their real-time accuracy and speed.