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Ryohei Sasaki@engineer

@rsasaki0109 • 9,301 subscribers

Software Engineer at MAP IV(TIER IV group) AI/Robotics/Autonomous Driving/GNSS/LiDAR/IMU/SLAM/Localization/Mapping

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ge-gnss-visibility GNSS satellite visibility simulation from Google Earth

ge-gnss-visibility GNSS satellite visibility simulation from Google Earth

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VLAExplain — Interpreting Vision-Language-Action (VLA) Models VLAExplain is an interpretability toolkit designed to help users visually understand the inner workings of Vision-Language-Action (VLA) models. Currently, attention analysis is supported for both the pi05 and unifolm-vla models. For details, please check pi05 and UnifoLM-VLA readme files respectively. Demo of pi05 in action:

VLAExplain — Interpreting Vision-Language-Action (VLA) Models VLAExplain is an interpretability toolkit designed to help users visually understand the inner workings of Vision-Language-Action (VLA) models. Currently, attention analysis is supported for both the pi05 and unifolm-vla models. For details, please check pi05 and UnifoLM-VLA readme files respectively. Demo of pi05 in action:

Ryohei Sasaki@engineer

12,774 views • 1 month ago

CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting CLM trains a 102-million Gaussian model on the MatrixCity BigCity Aerial Dataset under 4 hours using a single RTX 4090, achieving a PSNR of 25.

CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting CLM trains a 102-million Gaussian model on the MatrixCity BigCity Aerial Dataset under 4 hours using a single RTX 4090, achieving a PSNR of 25.

Ryohei Sasaki@engineer

34,517 views • 5 months ago

LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.

LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.

Ryohei Sasaki@engineer

14,935 views • 2 months ago

open_semantic_slam ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

open_semantic_slam ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

27,137 views • 1 year ago

ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

13,856 views • 1 year ago

TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle

TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle

sasaki@engineer

17,994 views • 1 year ago

LMDrive Closed-Loop End-to-End Driving with LLM An end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions

LMDrive Closed-Loop End-to-End Driving with LLM An end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions

10,085 views • 2 years ago

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