Inertia constrained visual odometry for navigational applications

Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in feature-poor environments, i.e. low-texture and/or low-light environments. In this paper, we aim to address the challenge of recovering rigid registration between successive camera poses in feature-poor environments in a Visual Inertial Odometry (VIO) setting. In addition to inertial sensing, we instrument a small aerial robot with an RGBD camera and propose a framework that unifies the incorporation of 3D geometric entities: points, lines, and planes. The tracked 3D geometric entities provide constraints in an Extended Kalman Filtering framework. We show that by directly exploiting 3D geometric entities, we can achieve improved registration. We demonstrate our approach on different texture-poor environments, with some containing only flat texture-less surfaces providing essentially no 2D features for tracking. In addition, we evaluate how the addition of different 3D geometric entities contributes to improved pose estimation by comparing an estimated pose trajectory to a ground truth pose trajectory obtained from a motion capture system. We consider computationally efficient methods for detecting 3D points, lines and planes, since our goal is to implement our approach on small mobile robots, such as drones.

Visual Odometry is widely used for recovering the trajectory of a vehicle in an autonomous navigation system. In this paper, we present an adaptive stereo visual odometry that separately estimates the rotation and translation. The basic framework of VISO2 is used here for feature extraction and matching due to its feature repeatability and real-time speed on standard CPU. The rotation is accurately obtained from the essential matrix of every two consecutive frames in order to avoid the affection of the stereo calibration uncertainty. With the estimated rotation, translation is rapidly calculated and refined by our proposed linear system with non-iterative refinement without the requirement of any ground truth data. The further improvement of the translation by joint backward and forward estimation is also presented in the same framework of the proposed linear system. The experimental results evaluated on the KITTI dataset demonstrate around 30% accuracy enhancement of the proposed scheme over the traditional visual odometry pipeline without much increase in the system overload.

Making large automatic device for polishing freeform surface of large mold has many disadvantages. We propose a new thinking of polishing large freeform surface with small mobile robot, and develop a novel small mobile polishing robot which has good maneuverability for moving on and polishing curved surface continuously. By analyzing its kinematics model, algorithm for tracking polishing path is designed, in which discrete points along planed polishing path on surface are taken as robot’s navigation points. And virtual robot view methods are designed in the tracking algorithm to avoid the oscillate phenomena. Both the simulation and field experiments prove that this type robot can satisfy the requirement of polishing process.

This paper deals with a small in-pipe mobile robot using the giant magnetostrictive alloy as actuators, which does not require any power supply codes. This cordless mobile robots can be controlled by the magnetic field supplied by the outer side. The mechanisms of the motions are based on the inch worm type of mobile mechanisms. In this paper, two mechanisms are shown: (1) the reversible motion and (2) the one-way motion mechnisms. The sizes of the robots are 21mm in diameter for the reversible motion and 6mm in diameter for the one way motion. The motion is controlled by the regulated motion of the outer electro-magnetic coil, so that the in-pipe mobile robot can follow the outer coil. This small mobile robot has many possible applications for the small pipe inspections, bio-medical fields and others.

The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matchers. To address this problem, a visual SLAM using deep feature matcher is proposed, which is mainly composed of three parallel threads: Visual Odometry, Backend Optimizer and LoopClosing. In the Visual Odometry, the deep feature extractor with convolutional neural networks is utilized for extracting feature points in each image frame. Then, the deep feature matcher is used for obtaining the corresponding feature landmark pairs. Afterwards, a fusion method based on the last and the reference frame is proposed for camera pose estimation. The Backend Optimizer is designed to execute local bundle adjustment for a part of camera poses and landmarks (map points). While LoopClosing, consisting of a lightweight deep loop closure detector and the same matcher as the one used in Visual Odometry is utilized for loop correction based on pose graph. The proposed system has been tested extensively on most of benchmark KITTI odometry dataset. The experimental results show that our system yields better performance than the existing visual SLAM systems. It can not only run in real-time at a speed of 0.08 s per frame, but also reduce estimation error by at least 0.1 m.

<abstract><p>For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of $ 2.211\% $ and a rotation error of $ 0.418\; ^{\circ}/100m $. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10$ \% $ intranslation error and 15$ \% $ in rotation error.</p></abstract>

We present a method that estimates the ego motion of a vehicle based on camera data of high resolution (207x204 pixels) Time-of-Flight cameras using visual odometry techniques. Translation and rotation of camera motion in six degrees of freedom are calculated. Point correspondences in consecutive amplitude image pairs are built. By consideration of the depth image of the camera 3D point correspondences are derived from the two dimensional point correspondences. Camera motion between the two images is then computed by registration of the two resulting point clouds. The process is optimized by incorporation of outlier removal and a multi sensor setup. The presented optimizations raise the precision and robustness of the method and enable visual odometry by Time-of-Flight camera data as an alternative to common odometry systems in low speed scenarios.

The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.

It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environments, assuming noiseless egocentric vision, noiseless actuation and most importantly, perfect localization. However, under realistic noise models for visual sensors and actuation, and without access to a "GPS and Compass sensor," the 99.6%-success agents for PointGoal navigation only succeed with 0.3%. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> In this work, we demonstrate the surprising effectiveness of visual odometry for the task of PointGoal navigation in this realistic setting, i.e., with realistic noise models for perception and actuation and without access to GPS and Compass sensors. We show that integrating visual odometry techniques into navigation policies improves the state-of-the-art on the popular Habitat PointNav benchmark by a large margin, improving success from 64.5% to 71.7% while executing 6.4 times faster.

Power consumption of tracked and wheeled small mobile robots on deformable terrains–model and experimental validation

Visual odometry (VO) is the process of estimating the ego-motion of an agent sequentially (e.g. vehicle, human, and robot) using only the input of single or multiple cameras attached to it. VO plays an important role in Simultaneous Localization and Mapping (SLAM), which has been widely applied in many fields. The development of VO would greatly promote the transition of visual mapping and navigation to the industry. This paper focuses on direct methods and aims to summarize recent direct algorithms from technical views. Image representation and frame matching strategy are the two key components of VO. This paper is organized based on these two aspects. A qualitative analysis of existing work is then presented. This paper simultaneously serves as a position and tutorial paper.

Visual odometry (VO) in underwater environments presents significant challenges due to poor visibility and dynamic scene changes, which render conventional (in-air) VO solutions unsuitable for underwater applications. We propose an underwater robust monocular visual odometry (UR-MVO) pipeline tailored for underwater scenarios with feature extraction and matching based on SuperPoint and SuperGlue models, respectively. We enhance the robustness of the feature extractor through field-specific fine-tuning of the SuperPoint model using few-shot unsupervised learning. This tuning was done on real images of underwater scenes in order to enhance its performance in the harsh underwater image conditions. Moreover, we integrate semantic segmentation trained on underwater images into our pipeline to eliminate unreliable features belonging to dynamic objects and background. We evaluated the proposed solution on the Aqualoc dataset, demonstrating higher localization accuracy compared to other SOTA direct and feature-based monocular VO methods like DSO and SVO and also obtained very competitive results compared to more resource-intensive monocular VSLAM approaches with loop closure process like LDSO, UVS, and ORB-SLAM. The results show a high potential for our approach for further applications in underwater exploration and mapping using affordable sensory setups. We publish the code for the benefit of the community https://github.com/be2rlab/UR-MVO

A useful application of event sensing is visual odometry, especially in settings that require high-temporal resolution. The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events. However, the cost scales with image resolution and the temporal resolution can be limited by the need for large batch sizes to yield sufficient structure in the contrast image <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . In this work, we propose spatiotemporal registration as a compelling technique for event-based rotational motion estimation. We theoretically justify the approach and establish its fundamental and practical advantages over contrast maximisation. In particular, spatiotemporal registration also produces feature tracks as a by-product, which directly supports an efficient visual odometry pipeline with graph-based optimisation for motion averaging. The simplicity of our visual odometry pipeline allows it to process more than 1 M events/second. We also contribute a new event dataset for visual odometry, where motion sequences with large velocity variations were acquired using a high-precision robot arm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetation or urban settlement preclude the conventional use of line-of-sight global navigation satellite systems (GNSS). In this paper, we evaluate unaided visual odometry, via an on-board stereo camera rig attached to the survey vessel, as a novel, low-cost localisation strategy. Feature-based and appearance-based visual odometry algorithms are implemented on a six degrees of freedom platform operating under guided motion, but stochastic variation in yaw, pitch and roll. Evaluation is based on a 663 m-long trajectory (>15,000 image frames) and statistical error analysis against ground truth position from a target tracking tachymeter integrating electronic distance and angular measurements. The position error of the feature-based technique (mean of ±0.067 m) is three times smaller than that of the appearance-based algorithm. From multi-variable statistical regression, we are able to attribute this error to the depth of tracked features from the camera in the scene and variations in platform yaw. Our findings inform effective strategies to enhance stereo visual localisation for the specific application of river monitoring.

Enhanced calibration of camera setups for high-performance visual odometry