Álvaro Gutiérrez, Re Campo, Francisco C. Santos et al. · 2009
Abstract: The improvement of odometry systems in collective robotics remains an important challenge for several applications. Social odometry is an online social dynamics which confers the robots the possibility to learn from the others. Robots neither share any movement constraint nor access to centralized information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance traveled. Social odometry guides a robot to its goal by imitating estimated locations, confidence levels and actual locations of its neighbors. This simple online social form of odometry is shown to produce a self-organized collective pattern which allows a group of robots to both increase the quality of individuals ’ estimates and efficiently improve their collective performance.
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Steven J. Harrison, M. T. Turvey · 2018
Cited by 1
David Nistér, Oleg Naroditsky, James Bergen · 2004
We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time. 1.
Cited by 0
David Nistér, Oleg Naroditsky, James Bergen · 2004
We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time. 1.
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Aaron Dutle, Bill Kay · Discrete Applied Mathematics · 2016
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Anders Hagnelius, Supervisor Cs-umu, Niclas Börlin · 2005
Visual odometry is the process of determining the position and orientation of a camera by analyzing a sequence of images from it. This can be useful for vehicle and robot navigation in locations where other techniques can not be used. In this master thesis a visual odometry system is implemented and evaluated. The system uses three low-cost web cameras mounted in a rig. The camera rig is calibrated i.e. the mutual position and orientation of the cameras are known. Two versions of the Förstner interest operator are used to find feature points in the images. The feature points are matched between the images and there corresponding object points are triangulated. The spatial matching is simplified with help of the known epipolar geometry. Four different triangulation methods are compared to see which gives the best precision of the system. The system uses a relation between the rigid body movement of the largest matchable subset of object points from two different camera positions. The optimal subset of object points is found by a ransac algorithm.
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D. Nistér, Oleg Naroditsky, James R. Bergen · 2004
We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time.
Cited by 978
Todor Stoyanov, Range Sensing Sonars, Kinematic Models et al. · 2016
Generally, odometry refers to the use of sensor data to measure the relative displacement between different positions. In robotics, odometry is typically used to refer to the use of wheel encoder, coupled with a forward kinematic model. Other types of odometry exist as well: e.g. visual odometry, or inertial measurement units (IMUs). The position and orientation (or pose) of the robot, given a certain motor encoder state, depends on the physical configuration of the robot.
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Kevin M. Judd, Jonathan D. Gammell · The International Journal of Robotics Research · 2024
Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object’s observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE(3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges.
Cited by 7Open access
Sebastian P. Kleinschmidt, Bernardo Wagner · 2018
For autonomous localization and navigation, a robot's ego-motion estimation is fundamental. RGB camera-based visual odometry (VO) has proven to be a robust technique used to determine a robot's motion. In situations when direct sunlight, the absence of light or presence of dust as well as smoke make vision difficult, RGB cameras may not provide a sufficient number of RGB features for an accurate and robust visual odometry. In contrast to the visual spectrum of light, imaging modalities like thermal cameras can still be used to identify a stable but small number of image features in the described situations. Unfortunately, the smaller number of image features results in a less accurate VO. In this paper, we present an approach to monocular visual odometry using multimodal image features of different imaging modalities as RGB, thermal and hyperspectral images. By using the strengths of various imaging modalities, the robustness and accuracy of VO can be drastically increased compared to traditional unimodal approaches. The presented method merges different motion hypotheses based on various imaging modalities to create a more accurate motion estimation as well as a map of multimodal image features. The uni- and multimodal motion estimations are evaluated regarding the absolute and relative trajectory errors. The results show that our multimodal approach works robustly in presence of partial sensor failures still creating a multimodal map containing image features of all modalities.
Cited by 11
Kevin H. Knuth, Ali Mohammad‐Djafari, Jean‐François Bercher et al. · AIP conference proceedings · 2011
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro‐Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Cited by 4
Xiuzhong Hu · TIB Data Manager · 2024
Cited by 0Open access
Minjie Lin, Qixin Cao, Haoruo Zhang · 2018
Accurate visual odometry is essential for many fields such as robot navigation and autonomous driving .In this paper, we propose a novel panoramic visual odometry algorithm that is real-time, precise and robust .The main contributions of our work are a series of innovations that address the challenge of effective initialization and robust feature tracking based on panoramic camera. Wide field of view (FOV) is very important for robotic perception. Our algorithm takes advantage of the 360° FOV of a panoramic camera, which results in high accuracy of camera pose estimation and feature tracking stability. We use panoramic images directly without converting them to pinhole images. Through GPU acceleration, our implementation runs at an average 30 frames per second on a consumer laptop. In addition, we have done both indoor and outdoor experiments to validate proposed algorithm, the results show that ... we call our approach PVO (panoramic visual odometry).
Cited by 8
BEATRIX TUNG XUE LIN · National University of Singapore · 2025
Master's
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Jakob Engel, Vladlen Koltun, Daniel Cremers · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017
Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.
Cited by 2,844
Davide Scaramuzza, Friedrich Fraundorfer · IEEE Robotics & Automation Magazine · 2011
Visual odometry (VO) is the process of estimating the egomotion of an agent (e.g., vehicle, human, and robot) using only the input of a single or multiple cameras attached to it. Application domains include robotics, wearable computing, augmented reality, and automotive. The term VO was coined in 2004 by Nister in his landmark paper [1]. The term was chosen for its similarity to wheel odometry, which incrementally estimates the motion of a vehicle by integrating the number of turns of its wheels over time. Likewise, VO operates by incrementally estimating the pose of the vehicle through examination of the changes that motion induces on the images of its onboard cameras. For VO to work effectively, there should be sufficient illumination in the environment and a static scene with enough texture to allow apparent motion to be extracted. Furthermore, consecutive frames should be captured by ensuring that they have sufficient scene overlap.
Cited by 1,468
Fanqi Xu, Yasin Almalıoğlu, Niki Trigoni · 2025
Learning-based Visual Odometry (VO) has seen significant advancements over the past decades. However, all the existing methods rely on the six degrees of freedom (6-DoF) representation for pose prediction, which is sparse and less conducive for neural network learning. In this work, we introduce a novel dense and distributed representation by modeling VO as ray bundles, referred to as RayVO. This richly parameterized representation is tightly coupled with corresponding spatial features, making it highly effective for neural learning. Additionally, the ray-based approach enables simultaneous prediction of both intrinsic and extrinsic parameters. To prove its effectiveness against the traditional 6-DoF representation, we propose three specialized loss functions for ray’s training: a ray-based loss, a 6-DoF-based loss and a hybrid loss. We extensively evaluate RayVO on both indoor and outdoor benchmark datasets and show that it outperforms the state-of-the-art VO methods.
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Kongsberg Maritime · Zenodo (CERN European Organization for Nuclear Research) · 2023
This is reduced resolution example data to accompany the code at `https://github.com/hflemmen/radar_odometry`.
Cited by 0Open access
Cheng Huang · PRISM (University of Calgary) · 2021
Over the last decade or so, the world has witnessed the rapid changes in the way people drive. How to ensure the navigation performance in challenging environments such as complex urban canyon environments or winter road environment with a relatively low-cost navigation system has become a popular research topic. Global Navigation Satellite System (GNSS) positioning is commonly used for land vehicle navigation. However, the accuracy of GNSS positioning is reduced in such challenging environments due to obstructions and multipath effects. Thus, the development of an alternative, accurate, inexpensive, and self-contained land vehicle navigation systems to bridge the GNSS gaps is significant for land vehicle navigation systems. Visual-inertial odometry (VIO) is an accurate, inexpensive, and complementary approach for land vehicle navigation in GNSS signal-denied environments. VIO is subject to scale drift because it estimates forward direction translation using distant feature points that are generally located only in the forward direction. Wheel odometer measurements can be obtained from the CANBUS interface of most modern passenger vehicles and these provide reliable estimates of the forward wheel speed. In this thesis, an innovative approach to incorporate wheel odometry (WO) and non-holonomic constraints (NHC) together with tightly-coupled monocular visual-inertial odometry using the Multi-State Constraint Kalman Filter (MSCKF) is proposed and implemented. The algorithm is first validated using the public KITTI Dataset [1] with simulated wheel odometer data. Then, the KAIST Complex Urban Dataset [2] is used to test the performance of IMU+Vision+WO integration system in urban canyon environments. Winter driving data is collected in Calgary and used to evaluate the influence of winter road conditions on the proposed algorithm. The results demonstrate that WO and NHC are able to control the scale drift, and as a result are able to control both scale and orientation over longer periods than IMU+Vision alone. IMU+Vision+WO achieved 1.814 m horizontal position error in a 1-minute drive in an urban canyon environment in the KAIST Complex Urban Dataset and 19.649 m and 3.456 m horizontal position errors in two 1-minute drives in our Calgary winter urban environment. The results demonstrate that IMU+Vision+WO is a very promising method to bridge the GNSS outages and performs very well in some challenging environments.
Cited by 0Open access
John Henawy · 2021
Municipal infrastructure inspection and maintenance are vital to keep the integrity and safe operation of large-scale infrastructure such as pipelines, bridges and tunnels. For example, regular inspection and assessment of tunnels are essential to prevent any possible leakage or worse, collapse. Inspection of deep sewerage tunnels is even more challenging because of their complex environment where such tunnels are not only difficult to reach, they may also have dangerous bacteria and microorganisms and the sewer gas would also have mixture of flammable and toxic gases such as hydrogen sulphide, methane, ammonia and sulphur dioxide. In Singapore, deep tunnel sewerage system (DTSS) is 6 m in diameter and located about 20 to 50 m underground. Recently, quadcopters are widely used for inspection because of their ability to do vertical take-off and landing with high stability, reach to great height as well as ease of use and low cost. However, quadcopter localization in deep sewerage tunnel is very challenging as the environment there is dark, wet, featureless and structureless. The darkness and featureless environment causes significant visual degradation, while structureless and wet environment causes lidar degradation. Thus, multiple sensors are required for proper robot localization to mitigate such sensor degradation. Moreover, employing an inertial measurement unit (IMU) as an additional sensor can dramatically improve both reliability and accuracy of visual and lidar odometry. Therefore, data fusion paradigm has become the major focus for the visual inertial odometry (VIO) and lidar inertial odometry (LIO) algorithms. Among these, the filtering data fusion paradigm is a common fusion technique because it is fast and efficient for online estimation. However, it produces cumulative linearization error which will cause gradual deterioration in the accuracy. In this thesis, a data fusion paradigm based on nonlinear optimization called a full smoothing paradigm has been proposed as a better alternative to the filtering paradigm. The full smoothing paradigm takes into consideration the full history of the previous states without marginalization. Therefore, it is more accurate than the filtering paradigms. However, high IMU sampling rate represents a great challenge for online estimation. This is because the full state history grows rapidly making online estimation not feasible. Motion preintegration overcomes this problem whereby the IMU measurements can be integrated into a single equivalent value between two visual or lidar keyframes. This makes the full smoothing paradigm simpler and the nonlinear optimization process can be done at a lower frequency. In this work, a novel IMU motion integration model was proposed for the continuous-time of IMU kinematics which are modelled using a switched linear system. The novel IMU model has a closed-form solution. Based on that, a novel IMU factor was formulated as a closed-form discrete factor to compute the mean measurements, covariance matrix and Jacobians. As such, the proposed model is more accurate and more efficient for online state estimation than the state-of-the-art. To evaluate the proposed IMU factor, it is integrated with the VIO and LIO frameworks as tightly coupled joint-optimization. The proposed VIO has been evaluated using simulated and real-world datasets. In addition, indoor experiments have been done to test its effectiveness. Results obtained show that the proposed framework outperforms the state-of-the-art approach by up to 22 % and 38 % on real datasets and indoor experiments, respectively. A novel nonlinear de-skewing is then proposed to correct the motion distortion between all scanned point in LIO. The novel IMU motion integration model is further used to correct the distorted points. In addition, the novel IMU factor is used with lidar factor in joint-optimization to obtain high state estimation. Results obtained show that the proposed model outperforms the state-of-the-art by up to 21 %.
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