High Dynamic Range SLAM with Map-Aware Exposure Time Control
The research in dense online 3D mapping is mostly focused on the geometrical accuracy and spatial extent of the reconstructions. Their color appearance is often neglected, leading to inconsistent colors and noticeable artifacts. We rectify this by extending a state-of-the-art SLAM system to accumulate colors in HDR space. We replace the simplistic pixel intensity averaging scheme with HDR color fusion rules tailored to the incremental nature of SLAM and a noise model suitable for off-the-shelf RGB-D cameras. Our main contribution is a map-aware exposure time controller. It makes decisions based on the global state of the map and predicted camera motion, attempting to maximize the information gain of each observation. We report a set of experiments demonstrating the improved texture quality and advantages of using the custom controller that is tightly integrated in the mapping loop.
Code (1)
Tasks
Vocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor
We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure. It …
MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation
Emerging 3D scene representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated their effectiveness in Simultaneous Localization and Mapping (SLAM) for photo-realistic renderi…
3DGSCamera LocalizationNeRFSimultaneous Localization and MappingEGS-SLAM: RGB-D Gaussian Splatting SLAM with Events
Gaussian Splatting SLAM (GS-SLAM) offers a notable improvement over traditional SLAM methods, enabling photorealistic 3D reconstruction that conventional approaches often struggle to achieve. However, existing GS-SLAM sy…
3D ReconstructionEdged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors
Conventional visual simultaneous localization and mapping (SLAM) algorithms often fail under rapid motion, low illumination, or abrupt lighting transitions due to motion blur and limited dynamic range. Event cameras miti…
Visual OdometryQueensCAMP: an RGB-D dataset for robust Visual SLAM
Visual Simultaneous Localization and Mapping (VSLAM) is a fundamental technology for robotics applications. While VSLAM research has achieved significant advancements, its robustness under challenging situations, such as…
Simultaneous Localization and Mapping