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

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 generalises 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 specialised 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 pretrained 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.