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...
The paper introduces a visual odometry frontend that automatically and continuously adapts its parameters using an image-conditioned reinforcement learning policy. The policy selects key tuning values—FAST detection threshold, KLT patch size, and RANSAC rejection threshold—based on a lightweight image embedding and frontend statistics, with a privileged critic aiding training. Trained on synthetic data, the approach transfers zero‑shot to real-world benchmarks, improving the tracking‑computation trade‑off by up to 8% in accuracy and 57% in runtime compared to static configurations.
SFVO is a stereo visual‑odometry framework that leverages pretrained stereo‑matching and optical‑flow models to obtain dense stereo and temporal correspondences. Rather than learning pose directly from images, it maps these correspondences into geometric constraints and predicts trustworthy points using decoupled confidence maps for rotation and translation. Experiments on both outdoor and indoor datasets show that SFVO delivers robust, accurate pose estimation with strong generalization, and the authors plan to release the code.
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.
The paper introduces a biologically inspired framework that learns object‑centric visual representations from raw videos without human annotations or camera calibration. By using motion boundaries detected via optical flow and clustering to create pseudo‑instance masks, the method supervises a single‑image encoder with pixel‑level pairwise metric learning. Training on 195 million pseudo‑labeled frames and expanding to 421 million frames through Motion‑Verified Self‑Training, the approach yields Swin‑based encoders that outperform or match supervised and self‑supervised baselines on tasks such as monocular depth estimation, 3D object detection, 3D occupancy prediction, and end‑to‑end planning.
arXiv:2608.29208v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models, built upon Vision-Language Models (VLMs), have significantly enhanced robotic capabilities by leveraging interne...
arXiv:2609.13733v1 Announce Type: new Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...
arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...
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.
Vision Foundation Models (VFMs) have significantly advanced dense feature matching, yet severe in-plane rotation remains a critical challenge. Existing solutions face a fundamental dilemma: data-driven methods require inefficient parameter scaling to implicitly learn rotations, whereas strictly equivariant networks lack the semantic capacity of modern VFMs.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
The paper introduces Driving with DINO (DwD), a framework that uses Vision Foundation Module (VFM) features to bridge simulation and real-world domains for autonomous driving video generation. It addresses the consistency‑realism dilemma by projecting VFM features onto a principal subspace, dropping high‑frequency texture elements, and applying a Random Channel Tail Drop to preserve structural detail. Additional components— a learnable Spatial Alignment Module and a Causal Temporal Aggregator— enhance control precision, spatial alignment, and temporal stability, reducing motion blur and ensuring realistic, consistent outputs.