State estimation is a central component of autonomous navigation. To date, many methods presented have a disruptive potential for application, such as visual-inertial odometry (VIO), wheel and leg odometry (for short, body odometry). However, most of them are prone to fail in some challenging conditions like high-dynamic street scenes and sustain aggressive movements. To this end, in this paper, we present a novel visual-inertial-velocity odometry (VIVO) framework which incorporates velocity measurement provided by the proprioceptive sensing into the MSCKF-based VIO in a tightly coupled fashion. Furthermore, considering that the imprecise extrinsic parameters can severely undermine the state estimation performance, we hence perform VIVO along with online calibration of the body odometry’s extrinsic parameters by adding them to the estimated state vector. The generic VIVO can be deployed for a broad spectrum of robot models ranging from wheeled robots to legged robots. Both simulation and real-world experiments are performed to extensively validate the robustness and accuracy of the proposed method in challenging scenarios using wheeled and legged robot models, respectively.

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird’s Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEV-ODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNN-MLP-based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy.

With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.