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 the pose estimation (i.e., determining the robot’s rotation and position) of skid-steering robots, since they change their orientation with an inevitable 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, etc. To prevent performance reduction in robots’ long-term 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. Note to Practitioners—This paper was motivated by the problem of long-term pose estimation of the commonly commercial-used skid-steering robots with only low-cost sensors. Skid-steering robots change their orientation with a skid, which poses a significant challenge for pose estimation when using the wheel encoders. We propose to online estimate the robot’s kinematics, which succeeds in compensating for the complexity of track-to-terrain interaction, due to the slippage, the imperfectness of mechanical design, terrain conditions and smoothness. It is critical to estimate the kinematics and poses jointly to prevent performance reduction in robots’ long-term missions. We further theoretically analyze whether the kinematics parameters can be estimated under different sensor configurations, and find out the special degrade motions that make the parameters unobservable.

In this work, we propose a novel method for performing inertial aided navigation, by using deep neural net-works (DNNs). To date, most DNN inertial navigation methods focus on the task of inertial odometry, by taking gyroscope and accelerometer readings as input and regressing for integrated IMU poses (i.e., position and orientation). While this design has been successfully applied on a number of applications, it is not of theoretical performance guarantee unless patterned motion is involved. This inevitably leads to significantly reduced accuracy and robustness in certain use cases. To solve this problem, we design a framework to compute observable IMU integration terms using DNNs, followed by the numerical pose integration and sensor fusion to achieve the performance gain. Specifically, we perform detailed analysis on the motion terms in IMU kinematic equations, propose a dedicated network design, loss functions, and training strategies for the IMU data processing, and conduct extensive experiments. The results show that our method is generally applicable and outperforms both traditional and DNN methods by wide margins.

In this article, we focus on pose estimation dedicated to nonholonomic ground robots with low-cost sensors, by probabilistically fusing measurements from wheel odometers and an exteroceptive sensor. For ground robots, wheel odometers are widely used in pose estimation tasks, especially in applications in planar scenes. However, since wheel odometer only provides two-dimensional (2D) motion measurements, it is extremely challenging to use that for accurate full 6-D pose (3-D position and 3-D orientation) estimation. Traditional methods for 6-D pose estimation with wheel odometers either approximate motion profiles at the cost of accuracy reduction, or rely on other sensors, e.g., inertial measurement unit, to provide complementary measurements. By contrast, we propose a novel motion-manifold-based method for pose estimation of ground robots, which enables to utilize wheel odometers for high-precision 6-D pose estimation. Specifically, the proposed method, first, formulates the motion manifold of ground robots by a parametric representation, second, performs manifold-based 6-D integration with wheel odometer measurements only, and third, reparameterizes manifold representation periodically for error reduction. To demonstrate the effectiveness and applicability of the proposed algorithmic module, we integrate that into a sliding-window pose estimator by using measurements from wheel odometers and a monocular camera. Extensive simulated and real-world experiments are conducted for evaluation, and the proposed algorithm is shown to outperform competing the state-of-the-art algorithms by a significant margin in pose estimation accuracy, especially when deployed in complex, large-scale real-world environments.

For commercially successful ground robots, high degree of autonomy, low manufacturing and maintenance cost, as well as minimized deployment limitations in different environments are essential attributes. To deliver an `anywhere deployable' product, it is impractical to rely on one single sensor or one single piece of algorithm to overcome all related challenges. Instead, the entire robotic system should be dedicated designed, including the choices of sensors, processors, algorithm integration for various functionality, and so on.This paper presents our design of perception system for commercial ground robots, which is able to operate in most common environments. The designed system is equipped with low-cost sensors and processors. The first key contribution of this paper is the design of the robotic sensory system, which includes a monocular camera, a 2D laser range finder (LRF), wheel encoders, and an inertial measurement unit (IMU). Our sensory system can be built at a cost of as low as $100. Furthermore, the selected sensors provide complementary characteristics for perception of both robot ego-motion and its surrounding environments, which are the prerequisites for `anywhere' deployment. The second key contribution of this paper is that a complete set of technologies is proposed based on our sensor systems, including sensor calibration (factory calibration and online calibration), localization (environmental exploring and re-localization), as well as mapping. The proposed methodology includes both efficient engineering implementation and theoretical novelty for high performance systems. Experimental results from our robotic testing platform and off-the-shelf commercial robots are presented. These results demonstrate that the proposed system can be deployed in various environmental conditions without performance compromise.

In this paper, we focus on the problem of vision-based localization for ground robotic applications. In recent years, camera only or camera-IMU (inertial measurement unit) based localization methods are widely studied, in terms of theoretical properties, algorithm design, and real-world applications. However, we experimentally find that none of existing methods is able to perform high-precision and robust localization for ground robots in large-scale complicated 3D environments. To this end, in this paper, we propose a novel vision-based localization algorithm dedicatedly designed for ground robots, by fusing measurements from a camera, an IMU, and the wheel odometer. The first contribution of this paper is that we propose a novel algorithm for approximating the motion manifold for ground robots by parametric representation and performing pose integrating via IMU and wheel odometer measurements. Secondly, we propose a complete localization algorithm, by using a sliding-window based estimator. The estimator is designed based on iterative optimization to fuse measurements from multiple sensors on the proposed manifold representation. We show that, based on a variety of real-world experiments, the proposed algorithm outperforms a number of the state-of-the-art vision based localization algorithms by a significant margin, especially when deployed in large-scale complicated environments.