Place recognition is indispensable for a drift-free localization system. Due to the variations of
the environment, place recognition using single-modality has limitations. In this paper, we …

In this paper, we propose a 3D object detection method called MMAF-Net that is based on
the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data …

张新钰, 邹镇洪, 李志伟, 等. 面向自动驾驶目标检测的深度多模态融合技术[J]. 智能系统学报,
2020, 15 (4): 758-771. DOI: 10.11992/tis. 202002010.ZHANG Xinyu, ZOU Zhenhong, LI …

Detecting small obstacles on the road is critical for autonomous driving. In this paper, we
present a method to reliably detect such obstacles through a multi-modal framework of …

Multisensor fusion-based road segmentation plays an important role in the intelligent driving
system since it provides a drivable area. The existing mainstream fusion method is mainly to …

This work presents a probabilistic deep neural network that combines LiDAR point clouds
and RGB camera images for robust, accurate 3D object detection. We explicitly model …

Autonomous driving requires accurate, robust, and fast decision-making perception systems
to understand the driving environment. Object detection is critical in allowing the perception …

3D object detection methods based on camera and LiDAR fusion are susceptible to
environmental noise. Due to the mismatch of physical characteristics of the two sensors, the …

Algorithms that fuse multiple input sources benefit from both complementary and shared
information. Shared information may provide robustness against faulty or noisy inputs, which …

In this paper, we introduce a new deep learning architecture for camera and Lidar sensor
fusion. The proposed scheme performs 2D object detection using the RGB camera image …