Point-cloud-based place recognition using CNN feature extraction
This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction. By using a convolutional neural network pre-trained on color images to extract features from a range image without fine-tuning on extra range images, significant improvement has been observed when compared to using hand-crafted features. The resulting system is illumination invariant, rotation invariant and robust against moving objects that are unrelated to the place identity. Apart from the system itself, we also bring to the community a new place recognition dataset containing both point cloud and grayscale images covering a full $360^\circ$ environmental view. In addition, the dataset is organized in such a way that it facilitates experimental validation with respect to rotation invariance or robustness against unrelated moving objects separately.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dyna…
3D Place RecognitionPoint Cloud RetrievalRetrievalVisual Place RecognitionHiTPR: Hierarchical Transformer for Place Recognition in Point Cloud
Place recognition or loop closure detection is one of the core components in a full SLAM system. In this paper, aiming at strengthening the relevancy of local neighboring points and the contextual dependency among global…
Loop Closure DetectionPoint Cloud RetrievalIrregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison
Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various indust…
Autonomous NavigationChange DetectionTDACloud: Point Cloud Recognition Using Topological Data Analysis
Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting meaningful local descriptors from a query point clou…
Autonomous DrivingGPUTopological Data AnalysisBEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images
Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high…
Retrieval