LiDAR-based 3D object detection and localization are crucial components of autonomous navigation systems, including autonomous vehicles and mobile robots. Most existing LiDAR-based 3D object detection and localization approaches primarily use geometric or structural feature abstractions from LiDAR point clouds. However, these approaches can be susceptible to environmental noise due to adverse weather conditions or the presence of highly scattering media. In this work, we propose an intensity aware voxel encoder for robust 3D object detection.
The proposed voxel encoder generates an intensity histogram that describes the distribution of point intensities within a voxel and is used to enhance the voxel feature set. We integrate this intensity-aware encoder into an efficient single-stage voxel-based detector for 3D object detection. Experimental results obtained using the KITTI dataset show that our method achieves comparable results with respect to the state-of-the-art method for car objects in 3D detection and from a bird’s-eye view and superior results for pedestrian and cyclic objects. Furthermore, our model can achieve a detection rate of 40.7 FPS during inference time, which is higher than that of the state-of-the-art methods and incurs a lower computational cost.
The pace of research into 3D vision perception has accelerated over the past few years, as it is an essential component of indoor and outdoor navigation systems. Examples of applications of navigation systems include autonomous vehicles (AVs) [1,2,3], robots [4,5], and augmented reality [6].
In regard to AVs, 3D perception in outdoor, urban environments still remains an open challenge [1,7,8]. This challenge is even greater in very complex scenarios, such as in dense intersections with a high volume of traffic and uncertain pedestrian actions.
To develop AVs that operate safely and in a hazard-free manner, it is important to understand what AVs perceive in environments that present on-road and off-road traffic objects, especially in dense and occluded environments. In AVs, 3D visual perception LiDAR and stereo camera sensors are considered the primary choices of sensing modalities.
Unlike 2D stereo camera images, LiDAR 3D point clouds provide accurate depth information about the surrounding objects, such as object scale, relative positions, and occlusion. However, due to the inherent sparsity and higher density variance in 3D point cloud data, it is very difficult to capture the geometric abstraction of objects.
To this end, different point-cloud-encoding techniques have been proposed that implement sparse-to-dense feature representation conversion while preserving geometric abstraction. The proposed encoders are then followed by 2D convolution filters for object detection and localization [9,10,11,12].
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