9 accepted papers
LiDAR odometry, fused by inertial measurement units (IMU), is an essential task in robotics navigation. Unlike the mainstream methods compensate the motion distortion of LiDAR data by high frequency inertial sensors, this paper deals with the distortion with continuous-time trajectory representation…
Imitation learning (IL) for end-to-end autonomous driving (E2E-AD) has made great progress recently in the closed-loop evaluation of the CARLA simulator. However, the causal confusion remains an open problem. To address this issue, we propose the BEVDrive-E2E to explore the interpretability of the e
Time series forecasting is of significant importance across various domains. However, it faces significant challenges due to distribution shift. This issue becomes particularly pronounced in online deployment scenarios where data arrives sequentially, requiring models to adapt continually to evolvin…
This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on variational autoencoders (VAEs) to generate discrete occupancy tokens, which inherently limit representational capacity, our approach predicts multi-fra
Freespace detection in autonomous driving is limited by the lack of explicit geometric modeling, hindering generalization across complex terrains. Existing approaches are predominantly data-driven and neglect the physical structure of drivable surfaces. We propose Terrain Flat (TerrFlat), a physics-…
We propose a Visual Place Recognition (VPR) framework by sharing lightweight keypoint extraction modules for local features. Current research on the joint learning of local keypoint matching and VPR is relatively scarce, and the application deployment of real-time spatial computing on edge devices h
Correspondence matching is a fundamental and crucial task in robot vision. In recent years, deep learning-based keypoint matching techniques have shown outstanding performance in downstream tasks. Conventional learning-based correspondence matching methods rely on large datasets and a specific train
Monocular Bird’s Eye View (BEV) semantic segmentation is critical for autonomous driving for its inherent advantages in spatial representation and downstream tasks. However, it is challenging to simultaneously learn view transformation and pixel-wise classification. Previous works suffer from non-fl…
In the context of 3D scene perception tasks, the significance of 3D occupancy prediction has been progressively growing, aiming to forecast the occupancy state of voxels in a discrete 3D space. However, existing methods typically exhibit several limitations, such as restricted adaptability to non-pi…