Models and methods originally developed for Novel View Synthesis and Scene Rendering, such as Neural Radiance Fields (NeRF) and Gaussian Splatting, are increasingly being adopted as representations in Simultaneous Localization and Mapping (SLAM). However, existing datasets fail to include the specific challenges of both fields, such as sequential operations and, in many settings, multi-modality in SLAM or generalization across viewpoints and illumination conditions in neural rendering. Additionally, the data are often collected using sensors which are handheld or mounted on drones or mobile robots, which complicates the accurate reproduction of sensor motions. To bridge these gaps, we introduce SLAM&Render, a novel dataset designed to benchmark methods in the intersection between SLAM, Novel View Rendering and Gaussian Splatting. Recorded with a robot manipulator, it uniquely includes 40 sequences with time-synchronized RGB-D images, IMU readings, robot kinematic data, and ground-truth pose streams. By releasing robot kinematic data, the dataset also enables the assessment of recent integrations of SLAM paradigms within robotic applications. The dataset features five setups with consumer and industrial objects under four controlled lighting conditions, each with separate training and test trajectories. All sequences are static with different levels of object rearrangements and occlusions. Our experimental results, obtained with several baselines from the literature, validate SLAM&Render as a relevant benchmark for this emerging research area.

翻译：最初为新颖视角合成与场景渲染开发的模型与方法（如神经辐射场与高斯泼溅）正日益被用作同步定位与建图的表征系统。然而，现有数据集未能涵盖这两个领域的特定挑战，例如SLAM中的序列操作与多模态特性，以及神经渲染中跨视角与光照条件的泛化能力。此外，数据采集通常使用手持式或搭载于无人机/移动机器人的传感器，这使传感器运动的精确复现变得复杂。为弥补这些空白，我们提出了SLAM&Render——一个专为SLAM、新颖视角渲染与高斯泼溅交叉领域方法评估而设计的新型数据集。该数据集通过机器人机械臂采集，独特地包含40条时序同步的RGB-D图像序列、IMU读数、机器人运动学数据及真实位姿流。通过公开机器人运动学数据，本数据集还能支持近期SLAM范式在机器人应用中的集成评估。数据集包含五种配置方案，涵盖消费级与工业级物体在四种受控光照条件下的场景，每条序列均配有独立的训练与测试轨迹。所有序列均为静态场景，具有不同层级的物体重排与遮挡配置。我们基于文献中多个基线方法获得的实验结果，验证了SLAM&Render作为这一新兴研究领域相关基准的有效性。