The paper introduces a minimalist visual-inertial odometry system that uses only four downward-facing photodiodes with optical Gabor masks and an IMU to estimate motion for differential-drive robots. By jointly optimizing mask parameters and a Temporal Convolutional Network in a physically-grounded simulator, the model decodes speed from the photodiode signals and combines it with IMU angular speed to produce a continuous planar trajectory. Experiments on a prototype robot across indoor and outdoor terrains show that the system closely follows reference trajectories without real-world fine-tuning.
By Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibitive GPU memory requirements, limiting their applicability in realistic, long-horizon scenarios. To address this, we present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system designed for large-scale outdoor scenes.
Existing object-aware SLAM systems force a trade-off between real-time performance, multi-class support, and the generation of high-fidelity, semantically coherent object models. To address this trade-off, we present DSP-SLAM++, which extends the DSP-SLAM framework with an asynchronous mapping pipeline for real-time performance and dedicated sensor fusion adaptations for a monocular fisheye-LiDAR suite.
arXiv:2608.29003v1 Announce Type: cross
Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...
By Wenting Wang, Jiaxin Guo, Wenzhen Dong, Yun-Hui Liu, Charlie C. L. Wang, Yeung Yam
arXiv:2608.23290v1 Announce Type: new
Abstract: Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely o...
By Antoni Valls, Jordi Sanchez-Riera
arXiv:2607.24495v2 Announce Type: replace
Abstract: Structured-light (SL) cameras power depth sensing in millions of devices, and recent neural SL decoding methods have substantially improved their d...
By Jiaheng Li, Binsheng Zhang, Xinhai Chang, Wenzheng Chen
Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environments such as construction sites. While LiDAR-based SLAM and occupancy grid methods offer viable solutions for detecting and tracking motion, many state-of-the-art 3D vision approaches rely heavily on pre-trained neural networks and require additional post-processing to identify moving objects.
arXiv:2608.24544v1 Announce Type: new
Abstract: Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimati...
By Renbiao Jin, Danping Zou, Wenxian Yu
Deep-learning features excel in visual matching, yet their practical value in tightly coupled visual-inertial SLAM (VI-SLAM) remains insufficiently characterized. We present DL-VINS-Factory, a unified framework that integrates learned feature extractors (ALIKED, RaCo, SuperPoint, XFeat) with either Lucas--Kanade (LK) optical-flow tracking or LightGlue (LG) descriptor matching.
arXiv:2607. 07885v1 Announce Type: cross Abstract: Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training data and simulation-based policies are impractical.
By Erik Jagnandan, Mulugeta Haile, Gregory Barber, Pratik Chaudhari
SLAM Adversarial Lab (SAL) is a modular framework designed to evaluate visual SLAM systems under adverse conditions such as fog, rain, camera, and video transport perturbations. It transforms existing datasets into adversarial versions by applying perturbations with severity levels expressed in real‑world units (e.g., meters for fog visibility). SAL’s extensible architecture separates datasets, perturbations, and SLAM algorithms via common interfaces, allowing users to add new components without rewriting integration code, and includes a search procedure to identify the severity at which a SLAM system fails.
By Mohamed Hefny, Karthik Dantu, Steven Y. Ko
arXiv:2607.08098v2 Announce Type: replace
Abstract: Event cameras are increasingly adopted in embodied perception for their microsecond temporal resolution, high dynamic range, and resilience to moti...
By Linli Shi, Ruijun Zhang, Ziyun Wang