One of the current trends in robotics is to employ large language models (LLMs) to provide
non-predefined command execution and natural human-robot interaction. It is useful to have …

We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and
accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or …

Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-
guided Loop Closure with Shared Latent Code, dubbed SLC $^ 2$-SLAM. Especially, we …

Simultaneous localization and mapping (SLAM) has achieved impressive performance in
static environments. However, SLAM in dynamic environments remains an open question …

Building accurate representations of the environment is critical for intelligent robots to make
decisions during deployment. Advances in photorealistic environment models have enabled …

Real-time multi-agent collaboration for ego-motion estimation and high-fidelity 3D
reconstruction is vital for scalable spatial intelligence. However, traditional methods produce …

In this paper, we present a novel algorithm for probabilistically updating and rasterizing
semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have …

Simultaneous Localization and Mapping (SLAM) is pivotal in robotics, with photorealistic
scene reconstruction emerging as a key challenge. To address this, we introduce …

Dense 3D representations of the environment have been a long-term goal in the robotics
field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for …