171 research outputs found
Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory
To build commercial robots, skid-steering mechanical design is of increased
popularity due to its manufacturing simplicity and unique mechanism. However,
these also cause significant challenges on software and algorithm design,
especially for pose estimation (i.e., determining the robot's rotation and
position), which is the prerequisite of autonomous navigation. While the
general localization algorithms have been extensively studied in research
communities, there are still fundamental problems that need to be resolved for
localizing skid-steering robots that change their orientation with a skid. To
tackle this problem, we propose a probabilistic sliding-window estimator
dedicated to skid-steering robots, using measurements from a monocular camera,
the wheel encoders, and optionally an inertial measurement unit (IMU).
Specifically, we explicitly model the kinematics of skid-steering robots by
both track instantaneous centers of rotation (ICRs) and correction factors,
which are capable of compensating for the complexity of track-to-terrain
interaction, the imperfectness of mechanical design, terrain conditions and
smoothness, and so on. To prevent performance reduction in robots' lifelong
missions, the time- and location- varying kinematic parameters are estimated
online along with pose estimation states in a tightly-coupled manner. More
importantly, we conduct in-depth observability analysis for different sensors
and design configurations in this paper, which provides us with theoretical
tools in making the correct choice when building real commercial robots. In our
experiments, we validate the proposed method by both simulation tests and
real-world experiments, which demonstrate that our method outperforms competing
methods by wide margins.Comment: 18 pages in tota
Planetary Rover Inertial Navigation Applications: Pseudo Measurements and Wheel Terrain Interactions
Accurate localization is a critical component of any robotic system. During planetary missions, these systems are often limited by energy sources and slow spacecraft computers. Using proprioceptive localization (e.g., using an inertial measurement unit and wheel encoders) without external aiding is insufficient for accurate localization. This is mainly due to the integrated and unbounded errors of the inertial navigation solutions and the drifted position information from wheel encoders caused by wheel slippage. For this reason, planetary rovers often utilize exteroceptive (e.g., vision-based) sensors. On the one hand, localization with proprioceptive sensors is straightforward, computationally efficient, and continuous. On the other hand, using exteroceptive sensors for localization slows rover driving speed, reduces rover traversal rate, and these sensors are sensitive to the terrain features. Given the advantages and disadvantages of both methods, this thesis focuses on two objectives. First, improving the proprioceptive localization performance without significant changes to the rover operations. Second, enabling adaptive traversability rate based on the wheel-terrain interactions while keeping the localization reliable.
To achieve the first objective, we utilized the zero-velocity, zero-angular rate updates, and non-holonomicity of a rover to improve rover localization performance even with the limited available sensor usage in a computationally efficient way. Pseudo-measurements generated from proprioceptive sensors when the rover is stationary conditions and the non-holonomic constraints while traversing can be utilized to improve the localization performance without any significant changes to the rover operations. Through this work, it is observed that a substantial improvement in localization performance, without the aid of additional exteroceptive sensor information.
To achieve the second objective, the relationship between the estimation of localization uncertainty and wheel-terrain interactions through slip-ratio was investigated. This relationship was exposed with a Gaussian process with time series implementation by using the slippage estimation while the rover is moving. Then, it is predicted when to change from moving to stationary conditions by mapping the predicted slippage into localization uncertainty prediction. Instead of a periodic stopping framework, the method introduced in this work is a slip-aware localization method that enables the rover to stop more frequently in high-slip terrains whereas stops rover less frequently for low-slip terrains while keeping the proprioceptive localization reliable
Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics
This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field
Analysis of Tread ICRs for Wheeled Skid-Steer Vehicles on Inclined Terrain
The instantaneous centers of rotation (ICRs) for the two treads of skid-steer vehicles moving with low inertia on hard horizontal terrain almost remain with constant local coordinates, which allows to establish an equivalence with differential-drive locomotion. However, this significant kinematic relationship has not been analyzed yet on sloped ground. One relevant difficulty of studying ICR behavior on inclined terrain, even on a flat surface, is the continuous variation of pitch and roll angles while turning. To overcome this problem, this paper analyzes a dynamic simulation of a skid-steer vehicle on horizontal ground where gravity is substituted by an equivalent external force in such a way that pitch and roll are kept constant. Relevant tread ICR variations on inclined ground have been deduced, which have a significant impact on skid-steer kinematics. These new findings have been corroborated experimentally with a four-wheeled mobile robot that turns on an inclined plane.Spanish Project PID2021-122944OB-I0
PIEKF-VIWO: Visual-Inertial-Wheel Odometry using Partial Invariant Extended Kalman Filter
Invariant Extended Kalman Filter (IEKF) has been successfully applied in
Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter,
showing great potential in sensor fusion. In this paper, we propose partial
IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie
group structure and apply it for Visual-Inertial-Wheel Odometry (VIWO) to
improve positioning accuracy and consistency. Specifically, we derive the
rotation-velocity measurement model, which combines wheel measurements with
kinematic constraints. The model circumvents the wheel odometer's 3D
integration and covariance propagation, which is essential for filter
consistency. And a plane constraint is also introduced to enhance the position
accuracy. A dynamic outlier detection method is adopted, leveraging the
velocity state output. Through the simulation and real-world test, we validate
the effectiveness of our approach, which outperforms the standard Multi-State
Constraint Kalman Filter (MSCKF) based VIWO in consistency and accuracy
Adaptive Localization and Mapping for Planetary Rovers
Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach
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