Autonomous localization in unknown environments is a fundamental problem in many emerging fields and the monocular visual approach offers many advantages, due to being a rich source of information and avoiding comparatively more complicated setups and multisensor calibration. Deep learning opened new venues for monocular odometry yielding not only end-to-end approaches but also hybrid methods combining the well studied geometry with specific deep components. In this paper we propose a monocular odometry that leverages deep depth within a feature based geometrical framework yielding a lightweight frame-to-frame approach with metrically scaled trajectories and state-of-the-art accuracy. The front-end is based on a multihypothesis matcher with perspective correction coupled with deep depth predictions that enables careful feature selection and tracking; especially of ground plane features that are suitable for translation estimation. The back-end is based on point-to-epipolar line minimization for rotation and unit translation estimation, followed by deep depth aided reprojection error minimization for metrically correct translation estimation. Furthermore, we also present a domain shift adaptation approach that allows for generalization over different camera intrinsic and extrinsic setups. The proposed approach is evaluated on the KITTI and KITTI-360 datasets, showing competitive results and in most cases outperforming other state-of-the-art stereo and monocular methods.

Accurate localization constitutes a fundamental building block of any autonomous system. In this article, we focus on stereo cameras and present a novel approach, dubbed SOFT2, that is currently the highest-ranking algorithm on the KITTI scoreboard. SOFT2 relies on the constraints imposed by the epipolar geometry and kinematics, i.e., it is developed for configurations that cannot exhibit pure rotation. We minimize point-to-epipolar-line distances, which makes the approach resilient to object depth uncertainty, and as the first step, we estimate motion up to scale using just a single camera. Then, we propose to jointly estimate the absolute scale and the extrinsic rotation of the second camera in order to alleviate the effects of varying stereo rig extrinsics. Finally, we smooth the motion estimates in a temporal window of frames by using the proposed epipolar line bundle adjustment procedure. We also introduce a multiple hypothesis feature-matching approach for self-similar planar surfaces that account for appearance change due to perspective. We evaluate SOFT2 and compare it to ORB-SLAM2, OV2SLAM, and VINS-FUSION on the KITTI-360 dataset, KITTI train sequences, Málaga Urban dataset, Oxford Robotics Car dataset, and Multivehicle Stereo Event Camera dataset.

Simultaneous localization and mapping (SLAM) is a core element of every autonomous mobile robot. The underlying engine of a SLAM system is its back-end, which aims at optimally estimating the trajectory and map of the environment based on sensor data abstractions. Over the past decade, SLAM solutions based on graph optimization approaches prevailed over the filtering based solutions, since they dominated in performance over a wider range of applications. In this paper we propose a novel filtering based SLAM back-end based on the exactly sparse delayed state filter (ESDSF) derived on Lie groups (LG-ESDSF). The proposed filter retains all the good characteristics of the classic ESDSF, but also respects the state space geometry by employing filtering equations directly on Lie groups. We have compared our SLAM system with two current state-of-the-art SLAM solutions, namely ORB-SLAM and LSD-SLAM, on the KITTI vision benchmark suite. Test results show that the proposed SLAM based on the LG-ESDSF back-end can achieve same level of accuracy as the methods based on the graph optimization techniques, while maintaining lower computation times.