BirdSLAM: Monocular Multibody SLAM in Bird's-Eye View (original) (raw)

Monocular visual odometry in urban environments using an omnidirectional camera

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

We present a system for Monocular Simultaneous Localization and Mapping (Mono-SLAM) relying solely on video input. Our algorithm makes it possible to precisely estimate the camera trajectory without relying on any motion model. The estimation is fully incremental: at a given time frame, only the current location is estimated while the previous camera positions are never modified. In particular, we do not perform any simultaneous iterative optimization of the camera positions and estimated 3D structure (local bundle adjustment). The key aspects of the system is a fast and simple pose estimation algorithm that uses information not only from the estimated 3D map, but also from the epipolar constraint. We show that the latter leads to a much more stable estimation of the camera trajectory than the conventional approach. We perform high precision camera trajectory estimation in urban scenes with a large amount of clutter. Using an omnidirectional camera placed on a vehicle, we cover the longest distance ever reported, up to 2.5 kilometers.

MonoSLAM: Real-time single camera SLAM

2007

Abstract We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the" pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches.

Robust monocular visual odometry for road vehicles using uncertain perspective projection

EURASIP Journal on Image and Video Processing, 2015

Many emerging applications in the field of assisted and autonomous driving rely on accurate position information. Satellite-based positioning is not always sufficiently reliable and accurate for these tasks. Visual odometry can provide a solution to some of these shortcomings. Current systems mainly focus on the use of stereo cameras, which are impractical for large-scale application in consumer vehicles due to their reliance on accurate calibration. Existing monocular solutions on the other hand have significantly lower accuracy. In this paper, we present a novel monocular visual odometry method based on the robust tracking of features in the ground plane. The key concepts behind the method are the modeling of the uncertainty associated with the inverse perspective projection of image features and a parameter space voting scheme to find a consensus on the vehicle state among tracked features. Our approach differs from traditional visual odometry methods by applying 2D scene and motion constraints at the lowest level instead of solving for the 3D pose change. Evaluation both on the public KITTI benchmark and our own dataset show that this is a viable approach for visual odometry which outperforms basic 3D pose estimation due to the exploitation of the largely planar structure of road environments.

On the use of inverse scaling in monocular SLAM

2009

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea.

Linear MonoSLAM: A linear approach to large-scale monocular SLAM problems

2014 IEEE International Conference on Robotics and Automation (ICRA), 2014

This paper presents a linear approach for solving monocular simultaneous localization and mapping (SLAM) problems. The algorithm first builds a sequence of small initial submaps and then joins these submaps together in a divideand-conquer (D&C) manner. Each of the initial submap is built using three monocular images by bundle adjustment (BA), which is a simple nonlinear optimization problem. Each step in the D&C submap joining is solved by a linear least squares together with a coordinate and scale transformation. Since the only nonlinear part is in the building of the initial submaps, the algorithm makes it possible to solve large-scale monocular SLAM while avoiding issues associated with initialization, iteration, and local minima that are present in most of the nonlinear optimization based algorithms currently used for large-scale monocular SLAM. Experimental results based on publically available datasets are used to demonstrate that the proposed algorithms yields solutions that are very close to those obtained using global BA starting from good initial guess.

Monocular vision SLAM for indoor aerial vehicles

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

This paper presents a novel indoor navigation and ranging strategy via monocular camera. By exploiting the architectural orthogonality of the indoor environments, we introduce a new method to estimate range and vehicle states from a monocular camera for vision-based SLAM. The navigation strategy assumes an indoor or indoor-like manmade environment whose layout is previously unknown, GPS-denied, representable via energy based feature points, and straight architectural lines. We experimentally validate the proposed algorithms on a fully self-contained microaerial vehicle (MAV) with sophisticated on-board image processing and SLAM capabilities. Building and enabling such a small aerial vehicle to fly in tight corridors is a significant technological challenge, especially in the absence of GPS signals and with limited sensing options. Experimental results show that the system is only limited by the capabilities of the camera and environmental entropy.

On Monocular Visual Odometry for Indoor Ground Vehicles

2012 Brazilian Robotics Symposium and Latin American Robotics Symposium, 2012

In this work, a Visual Odometry system estimating camera position and orientation based solely on image meausurements is proposed. The system is built on the fundamentals of Structure from Motion theory, requiring only a single calibrated camera to estimate positional information. Experiments were conducted on publicly available datasets with associated Ground Truth, in which a consumer grade webcamera was employed as the system's sensor on realistic indoor scenarios. The proposed system resulted in absolute localization error rates ranging from 2.10% to 3.39% of the travelled distance at processing rates of up to 5Hz, performing better than wheel odometry in certain situations and being equiparable to similar approaches present in the robotics literature.

Vehicle absolute ego-localization from vision, using only pre-existing geo-referenced panoramas

RelStat, 2019

Precise ego-localization is an important issue for Intelligent Vehicles. Geo-positioning with standard GPS often has localization error up to 10 meters, and is even sometimes unavailable due to "urban canyon" effect. It is therefore an interesting goal to design an affordable and robust alternative to GPS ego-localization. In this paper, we present 2 approaches for absolute ego-localization based on vision only, and not requiring previous driving on same street, by leveraging only pre-existing geo-referenced panoramas such as those from Google StreetView. Our first variant is based on Bag of visual Words + visual keypoints matching + bundle adjustment, and the other one uses direct pose regression computed by a deep Convolutional Neural Network (CNN) taking the query image as input. We have evaluated our 2 proposed variants using a real car. On around 1 km in a dense urban area, we obtained average localization errors of 2.8m with visual keypoints-matching + geometric computations, and of 7.7m with pose regression using pre-trained deep CNN. This shows that our proposed approaches are therefore potentially interesting complements or even alternatives to GPS localization.

Monocular Visual Inertial Direct SLAM with Robust Scale Estimation for Ground Robots/Vehicles

Robotics, 2021

In this paper, we present a novel method for visual-inertial odometry for land vehicles. Our technique is robust to unintended, but unavoidable bumps, encountered when an off-road land vehicle traverses over potholes, speed-bumps or general change in terrain. In contrast to tightly-coupled methods for visual-inertial odometry, we split the joint visual and inertial residuals into two separate steps and perform the inertial optimization after the direct-visual alignment step. We utilize all visual and geometric information encoded in a keyframe by including the inverse-depth variances in our optimization objective, making our method a direct approach. The primary contribution of our work is the use of epipolar constraints, computed from a direct-image alignment, to correct pose prediction obtained by integrating IMU measurements, while simultaneously building a semi-dense map of the environment in real-time. Through experiments, both indoor and outdoor, we show that our method is rob...