5 citations · 11 across the 12 of their papers we have counts for
12 papers
DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Pengxuan Yang, Yupeng Zheng, Deheng Qian +11
We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps…
General Place Recognition Survey: Towards Real-World Autonomy
Peng Yin, Jianhao Jiao, Shiqi Zhao +5
In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a corners…
BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition
Peng Yin, Abulikemu Abuduweili, Shiqi Zhao +2
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas. Unlike hum…
Temporal Point Cloud Completion with Pose Disturbance
Jieqi Shi, Lingyun Xu, Peiliang Li +2
Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this w…
Graph-Guided Deformation for Point Cloud Completion
Jieqi Shi, Lingyun Xu, Liang Heng +1
For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is g…
PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding
Peng Yin, Lingyun Xu, Ziyue Feng +2
Accurate localization on autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments…
3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
Peng Yin, Lingyun Xu, Jianmin Ji +2
One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To…
i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions
Peng Yin, Lingyun Xu, Ji Zhang +2
We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invar…
MRS-VPR: a multi-resolution sampling based global visual place recognition method
Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen +7
Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving l…
A Multi-Domain Feature Learning Method for Visual Place Recognition
Peng Yin, Lingyun Xu, Xueqian Li +6
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and…
Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection
Peng Yin, Yuqing He, Lingyun Xu +3
Loop Closure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are u…
Towards Stable Adversarial Feature Learning for LiDAR based Loop Closure Detection
Lingyun Xu, Peng Yin, Haibo Luo +2
Stable feature extraction is the key for the Loop closure detection (LCD) task in the simultaneously localization and mapping (SLAM) framework. In our paper, the feature extraction…