Related papers: GSLAM: A General SLAM Framework and Benchmark
Collaborative simultaneous localization and mapping (CSLAM) is essential for autonomous aerial swarms, laying the foundation for downstream algorithms such as planning and control. To address existing CSLAM systems' limitations in relative…
Autonomous exploration in unknown environments remains a fundamental challenge in robotics, particularly for applications such as search and rescue, industrial inspection, and planetary exploration. Multi-robot active SLAM presents a…
We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the…
A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world…
Simulation engines are widely adopted in robotics. However, they lack either full simulation control, ROS integration, realistic physics, or photorealism. Recently, synthetic data generation and realistic rendering has advanced tasks like…
In recent decades, several assistive technologies have been developed to improve the ability of blind and visually impaired (BVI) individuals to navigate independently and safely. At the same time, simultaneous localization and mapping…
Existing Simultaneous Localization and Mapping (SLAM) approaches are limited in their scalability due to growing map size in long-term robot operation. Moreover, processing such maps for localization and planning tasks leads to the…
The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental…
The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and costs in terms of computation or pricing. Since the demand…
Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing methods still struggle to jointly achieve low-latency pose…
LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input…
The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited…
We propose a novel visual SLAM method that integrates text objects tightly by treating them as semantic features via fully exploring their geometric and semantic prior. The text object is modeled as a texture-rich planar patch whose…
Cross-platform development solutions can help to make software available on different devices and platforms. But these are normally restricted to preconfigured platforms and consider that each individual solution is equal or similar to each…
LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) serves as a basis for indoor cleaning, navigation, and many other useful applications in both industry and household. From a series of LiDAR scans, it…
Serving large language models (LLMs) in production can incur substantial costs, which has prompted recent advances in inference system optimizations. Today, these systems are evaluated against conventional latency and throughput metrics…
Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluation frameworks. We present MapEval, an open-source framework…
This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset…
Performing an overview of the benchmarking initiatives oriented towards the performance evaluation of Holonic Manufacturing Systems shows that there are very few of them. However, a comparison between all the isolated emu-lation…
A robust visual localization and mapping system is essential for warehouse robot navigation, as cameras offer a more cost-effective alternative to LiDAR sensors. However, existing forward-facing camera systems often encounter challenges in…