Sensors, SLAM and Long-term Autonomy: A Review

Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge

Systems of Geo Positioning of the Mobile Robot

Article is devoted to the analysis of opportunities of electronic instruments, such as a gyroscope, the accelerometer, the magnetometer together, the video system of image identification and system of infrared indicators during creation of system of exact positioning of the mobile robot. Results of testing and the operating algorithms are given. Possibilities of sharing of these devices and their association in a single system are analyzed. Conclusions on development of opportunities and elimination of shortcomings of the received end-to-end system of positioning of the robot are drawn

Monocular navigation for long-term autonomy

We present a reliable and robust monocular navigation system for an autonomous vehicle. The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS. Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach. In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled. We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound. The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes. This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation

Navigation without localisation: reliable teach and repeat based on the convergence theorem

We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri

Navigational Drift Analysis for Visual Odometry

Visual odometry estimates a robot's ego-motion with cameras installed on itself. With the advantages brought by camera being a sensor, visual odometry has been widely adopted in robotics and navigation fields. Drift (or error accumulation) from relative motion concatenation is an intrinsic problem of visual odometry in long-range navigation, as visual odometry is a sensor based on relative measurements. General error analysis using ``mean'' and ``covariance'' of positional error in each axis is not fully capable to describe the behavior of drift. Moreover, no theoretic drift analysis is available for performance evaluation and algorithms comparison. Drift distribution is established in the paper, as a function of the covariance matrix from positional error propagation model. To validate the drift model, experiment with a specific setting is conducted