An overview of real-time 3D object detection for mobile robots: towards multi-sensor fusion

In recent years, 3D object detection has attracted considerable interest from researchers due to its crucial importance in scene understanding tasks for mobile robots. The task of 3D object detection is to determine the position, orientation, scale, and category of objects in a scene, with multimodal data inputs. These inputs consist of two distinct types: images and point clouds. Images provide colour information about the scene, while point clouds deliver geometric information. The fusion of these two diverse data types can enhance the detection of 3D objects by allowing them to complement each other. In this paper, a review of real-time multimodal 3D object detection from the perspective of multimodal sensor fusion is presented. It is important to note that previous reviews of multimodal 3D object detection have been limited to autonomous driving scenarios. In contrast, this paper reviews multimodal 3D object detection approaches in both indoor and outdoor scenarios. In addition, the popular datasets and assessment metrics employed for 3D object detection are described, as well as the issues and future prospects of 3D object detection.