AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work presents BPC-SLAM, an RGB-D SLAM system featuring part-level dynamic suppression and structure-constrained optimization for dynamic human environments, and improves the robustness and accuracy of pose estimation by incorporating human structural constraints.
The proposed Semantic and Geometric Adaptive SLAM system effectively suppresses dynamic artifacts and point-cloud contamination in dense mapping, generating static environment maps with clearer structures and improved geometric consistency.
A motion uncertainty estimation method is proposed to derive uncertainty from the residual between optical flow and camera motion induced flow, directly reflecting object motion, to derive uncertainty from the residual between optical flow and camera motion induced flow for dynamic scenes.
This work proposes a real-time RGB Dynamic SLAM framework that precisely removes moving objects at the pixel level without relying on predefined labels or segmentation models, and significantly improves upon state-of-the-art dense dynamic SLAM approaches (0.5-3 FPS).
A temporally maintained object-level mask and optical-flow consistency are used to filter unreliable observations, while 3D line landmarks provide complementary structural constraints from the static environment for robust localization in indoor scenes with moving objects, without relying on GPU acceleration.
Reliable localization and mapping are critical for intelligent robotic systems operating in dynamic indoor environments, where pedestrians and other moving objects can lead to erroneous feature associations, map contamination, and accumulated trajectory drift. To address these challenges, this study proposes the Bounda...
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