5 accepted papers
Cooperative perception allows agents to extend perceptual capabilities through inter-agent communication. However, most existing methods still adopt a frame-wise paradigm, which limits the exploitation of spatio-temporal consistency in dynamic scenes. Therefore, when observations are partially occlu
Accurate ego-centric localization assumes a paramount significance in the domain of autonomous driving. However, traditional methods for camera-LiDAR map localization rely on perspective projection to create a unified representation, which often falls short due to challenges such as occlusion and th…
The effective reasoning of integrated multimodal perception information is crucial for achieving enhanced end-to-end autonomous driving performance. In this paper, we introduce a novel multitask imitation learning framework for end-to-end autonomous driving that leverages a dual attention transforme…
Existing LiDAR odometry strategies match a new scan iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (BA) cannot be directly introduced into odometry due to the intensive computation of global land…
In this paper, a spectral-spatial symmetrical aggregation cross-linking network (SACLNet) is developed for multi-modal data classification, which contains three modules as follows. First, the Spectro-Spatial Feature Learning Module is proposed, using the involution operation sliding over the spectra…