UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment

• URL: http://arxiv.org/abs/2506.07013v1
• Date: Sun, 08 Jun 2025 06:30:37 GMT
• Title: UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment
• Authors: Wentao Zhao, Yihe Niu, Yanbo Wang, Tianchen Deng, Shenghai Yuan, Zhenli Wang, Rui Guo, Jingchuan Wang,
• Abstract summary: We present UNO, a unified visual odometry framework that enables robust and pose estimation across diverse environments.<n>Our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices.<n>We extensively evaluate our method on three major benchmark datasets.
• Score: 22.92093036869778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices. To this end, we introduce a Mixture-of-Experts strategy for local state estimation, with several specialized decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-Softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder, and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pre-trained, scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones), and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance.

Related papers

• Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos [58.156141601478794]
  Multi-object tracking (UAVT) aims to track multiple objects while maintaining consistent identities across frames of a given video.<n>Existing methods typically model motion cues and appearance separately, overlooking their interplay and resulting in suboptimal tracking performance.<n>We propose AMOT, which exploits appearance and motion cues through two key components: an Appearance-Motion Consistency (AMC) matrix and a Motion-aware Track Continuation (MTC) module.
  arXiv  Detail & Related papers  (2025-08-03T12:06:47Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [58.67129770371016]
  We propose a novel IRSTD framework that reimagines the IRSTD paradigm by incorporating textual metadata for scene-aware optimization.<n>AuxDet consistently outperforms state-of-the-art methods, validating the critical role of auxiliary information in improving robustness and accuracy.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics [14.694200929205975]
  This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. The proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions.
  arXiv  Detail & Related papers  (2024-08-08T08:33:02Z)
• GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding [8.289857214449372]
  GenDepth is a novel model capable of performing metric depth estimation for arbitrary vehicle-camera setups. We propose a novel embedding of camera parameters as the ground plane depth and present a novel architecture that integrates these embeddings with adversarial domain alignment. We validate GenDepth on several autonomous driving datasets, demonstrating its state-of-the-art generalization capability for different vehicle-camera systems.
  arXiv  Detail & Related papers  (2023-12-10T22:28:34Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications [0.0]
  Multi-object tracking (MOT) is a crucial component of situational awareness in military defense applications. We present a robust object tracking architecture aimed to accommodate for the noise in real-time situations. We propose a kinematic prediction model, called Deep Extended Kalman Filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space.
  arXiv  Detail & Related papers  (2021-10-05T13:50:38Z)
• Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency [114.02182755620784]
  We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our framework is shown to outperform the state-of-the-art depth and motion estimation methods.
  arXiv  Detail & Related papers  (2021-02-04T14:26:42Z)
• Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
  We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
  arXiv  Detail & Related papers  (2020-12-09T15:47:21Z)
• Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation [89.82169646672872]
  We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori. We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
  arXiv  Detail & Related papers  (2020-11-30T20:46:48Z)
• ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [54.33327082243022]
  ClusterVO is a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynamic object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving.
  arXiv  Detail & Related papers  (2020-03-29T09:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.