8 accepted papers
In safe reinforcement learning (SRL), there exists an inherent conflict between maximizing reward and minimizing cost. We propose a novel approach that effectively resolve the conflict between maximizing reward and minimizing cost in joint optimization.When the cost exceeds the threshold, we perform
Autonomous exploration in complex environments is frequently hindered by inefficient back-and-forth movements and repetitive revisits to previously explored areas. To address these drawbacks, we propose a two-mode hybrid dynamic exploration strategy that detects isolated frontier clusters and adapti…
Traditional SLAM performs well in static environments, but experiences degeneration of localization accuracy and stability in dynamic settings. To enhance performance in dynamic environments, this letter presents DGO-VINS, a real-time dynamic visual-inertial SLAM system based on geometric constraint
Autonomous exploration in large environments often leads to inefficient long backtracking, as distant targets are prioritized over closer ones. In this work, a hierarchical planning method is proposed, which employs region partitioning to systematically address the aforementioned issue. The space is
The performance of a visual SLAM system based on point features significantly diminishes in low-textured environments due to the challenges in extracting sufficient and reliable points. The fusion of line and point features improves SLAM system performance by providing additional visual constraints.
RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, the…
Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationship…
"Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space. (2) The fixed number of prototypes from subjective assumptions ignores the data feature differences and diversity. To over…