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Found 9 papers, 5 papers with code
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation.
SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities.
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition methods.
GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors
LiDAR place recognition approaches based on deep learning suffer from significant performance degradation when there is a shift between the distribution of training and test datasets, often requiring re-training the networks to achieve peak performance.
Uncertainty-Aware Lidar Place Recognition in Novel Environments
State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments.
InCloud: Incremental Learning for Point Cloud Place Recognition
In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space.
Point Cloud Segmentation Using Sparse Temporal Local Attention
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment.
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.
Ranked #37 on
Self-Supervised Action Recognition
on UCF101
