17 citations · 84 across the 21 of their papers we have counts for
21 papers
360FusionNeRF: Panoramic Neural Radiance Fields with Joint Guidance
Shreyas Kulkarni, Peng Yin, Sebastian Scherer
We present a method to synthesize novel views from a single panorama image based on the neural radiance field (NeRF). Prior studies in a similar setting rely on the nei…
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
Jingtian Yan, Xingqiao Lin, Zhongqiang Ren +6
Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as rob…
iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images
Peng Yin, Ivan Cisneros, Ji Zhang +2
The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant mo…
General Place Recognition Survey: Towards the Real-world Autonomy Age
Peng Yin, Shiqi Zhao, Ivan Cisneros +6
Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The…
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…
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
Ivan Cisneros, Peng Yin, Ji Zhang +2
We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The da…
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
Peng Yin, Haowen Lai, Shiqi Zhao +4
We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile t…
SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor
Shiqi Zhao, Peng Yin, Ge Yi +1
LiDAR-based localization approach is a fundamental module for large-scale navigation tasks, such as last-mile delivery and autonomous driving, and localization robustness highly re…
ALITA: A Large-scale Incremental Dataset for Long-term Autonomy
Peng Yin, Shiqi Zhao, Ruohai Ge +5
For long-term autonomy, most place recognition methods are mainly evaluated on simplified scenarios or simulated datasets, which cannot provide solid evidence to evaluate the readi…
AdaFusion: Visual-LiDAR Fusion with Adaptive Weights for Place Recognition
Haowen Lai, Peng Yin, Sebastian Scherer
Recent years have witnessed the increasing application of place recognition in various environments, such as city roads, large buildings, and a mix of indoor and outdoor places. Th…
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Ziyue Feng, Longlong Jing, Peng Yin +2
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfacto…
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…
FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans
Peng Yin, Jianing Qian, Yibo Cao +2
Acquiring accurate three-dimensional depth information conventionally requires expensive multibeam LiDAR devices. Recently, researchers have developed a less expensive option by pr…
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…
Learning to Sequence Robot Behaviors for Visual Navigation
Hadi Salman, Puneet Singhal, Tanmay Shankar +6
Recent literature in the robotics community has focused on learning robot behaviors that abstract out lower-level details of robot control. To fully leverage the efficacy of such b…
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…
Condition directed Multi-domain Adversarial Learning for Loop Closure Detection
Peng Yin, Yuqing He, Na Liu +1
Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are us…