Abstract
Deformable scenes violate the rigidity assumptions underpinning classical visual--inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or to severe camera pose drift when deformation dominates visual parallax. In this paper, we introduce DefVINS, the first visual-inertial odometry pipeline designed to operate in deformable environments. Our approach models the odometry state by decomposing it into a rigid, IMU-anchored component and a non-rigid scene warp represented by an embedded deformation graph. As a second contribution, we present VIMandala, the first benchmark containing real images and ground-truth camera poses for visual-inertial odometry in deformable scenes. In addition, we augment the synthetic Drunkard's benchmark with simulated inertial measurements to further evaluate our pipeline under controlled conditions. We also provide an observability analysis of the visual-inertial deformable odometry problem, characterizing how inertial measurements constrain camera motion and render otherwise unobservable modes identifiable in the presence of deformation. This analysis motivates the use of IMU anchoring and leads to a conditioning-based activation strategy that avoids ill-posed updates under poor excitation. Experimental results on both the synthetic Drunkard's and our real VIMandala benchmarks show that DefVINS outperforms rigid visual--inertial and non-rigid visual odometry baselines. Our source code and data will be released upon acceptance.
Explore similar work
Jul 24, 2026cs.RO
Visual inertial odometry (VIO) is essential for accurate 6-DoF motion estimation in mobile robotic systems. Recent learning-based VIO methods have shown promising progress, but they often rely on unified visual--inertial representations and a single temporal model for full-pose estimation, limiting their ability to capture the heterogeneous dynamics of rotation and translation. Moreover, monocular visual features often lack explicit geometric structure, while raw inertial encoding leaves the underlying rotational kinematics implicit, weakening the rotation-related cues in IMU features. To address these issues, we propose DB-VIO, a dual-branch visual inertial odometry framework with enhanced visual--inertial representation. DB-VIO incorporates depth cues to improve monocular visual perception, injects an explicit integrated-attitude prior to strengthen rotation-aware inertial representation, and decouples pose estimation into dedicated rotational and translational branches for motion-specific temporal modeling. Experiments on autonomous driving and aerial robot benchmarks show that DB-VIO achieves state-of-the-art performance, improving the corresponding baselines by 20% on KITTI and 33% on EuRoC. Notably, under the more agile motion patterns of EuRoC, DB-VIO improves the rotational metric by 65.7% over prior methods. These results demonstrate the effectiveness and generalization of DB-VIO across different platforms and motion scenarios.
Ziyu Wan, Lin Zhao
Sep 8, 2026cs.RO
A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective way to fulfill a low-cost sensor configuration. Using this sensor configuration, visual-inertial system (VINS) focuses on fusing data from a camera and an IMU sensor to estimate the six degrees-of-freedom (DOF) of the sensor pose. Typically, VINS uses only a single camera as visual input, which lead to problems such as error accumulation due to occlusion, various illumination, and textureless environments. In this paper, we propose a new multiple fisheye camera-based visual-inertial system called MFVINS. We present an IMU-aided FAST feature tracker for multiple cameras that enables efficient extraction and robust matching of local features. Then, the proposed method filters out outliers caused by fisheye distortion on the normalized image plane. Subsequently, a new reprojection error with physical validity constraints is proposed for bundle adjustment using learning-based depth estimation. The proposed method is applied to various scenarios, and its effectiveness is demonstrated by comparing previous VINS methods. In particular, MFVINS is implemented in real-time process to leverage the advantages of using multiple cameras -- robustness against occlusion and textureless regions -- while reducing the computational burden.
Eunseong Jang, YuJin Chung, Sang Jun Lee +2
May 19, 2026cs.RO
Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.
Francesco Pasti, Jeremy Klotz, Nicola Bellotto +1