Michał Nowicki

Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform

Consumer electronics mobile devices, such as smartphones or tablets, are rapidly growing in computing power and are equipped with an increasing number of sensors. This enables to use a present-day mobile device as a viable platform for computation-intensive, real-time applications in navigation and guidance. In this paper, we present a study on the performance of the orientation estimation based on the data acquired by the accelerometer, magnetometer, and gyroscope in a mobile device. Reliable orientation estimation based on the readouts from inertial sensors may be used in more complex systems, e.g., to correct the orientation error of a visual odometry system. We present a rigorous derivation of the mathematical estimation model, and we thoroughly evaluate the performance of the orientation estimation mechanism available in the Android OS, and the proposed alternative solutions on an unique dataset gathered using an actual smartphone. From the experimental results, we draw the conclusions as to the best performing algorithm, and then we evaluate its execution time on Android-based devices to demonstrate the possibility of real-time usage. The Android code for the proposed orientation estimation system is made publicly available for scientific and commercial applications.

LAURON V: A versatile six-legged walking robot with advanced maneuverability

Adaptive multi-legged walking robots are predestined to be applied in rough and hazardous terrain. Their walking and climbing skills allow them to operate at places that are unreachable for most wheeled vehicles. In this paper, we present the design and development of the new six-legged walking robot LAURON V with its improved kinematics and robust mechanical structure. Each leg has four independent joints that enable LAURON to cope with steep inclines, large obstacles and makes it possible to manipulate objects with its front legs. Autonomy, robustness and a large payload capacity together with its impressive terrain adaptability make LAURONV highly suitable for all kinds of field applications.

Combining photometric and depth data for lightweight and robust visual odometry

This paper presents a visual odometry system for mobile robots that works on RGB-D data from a Kinect/Xtion class sensor, and reports experimental results of evaluating this system on publicly available data. The aim of the presented research was to build a lightweight RGB-D visual odometry system, which can run in real-time on-board of such robots as walking machines that have limited computing resources. The proposed approach is based on tracking FAST keypoints over a sequence of RGB frames to establish correspondences between the photometric features in selected keyframes of the RGB-D data stream, and then on using the readily available depth data to map these features into 3D coordinates. The approach is tested on publicly available data, demonstrating satisfying performance with very low requirements as to the computing resources.

On the Performance of Pose-Based RGB-D Visual Navigation Systems

This paper presents a thorough performance analysis of several variants of the feature-based visual navigation system that uses RGB-D data to estimate in real-time the trajectory of a freely moving sensor. The evaluation focuses on the advantages and problems that are associated with choosing a particular structure of the sensor-tracking front-end, employing particular feature detectors/descriptors, and optimizing the resulting trajectory treated as a graph of sensor poses. Moreover, a novel yet simple graph pruning algorithm is introduced, which enables to remove spurious edges from the pose-graph. The experimental evaluation is performed on two publicly available RGB-D data sets to ensure that our results are scientifically verifiable.

An Indoor RGB-D Dataset for the Evaluation of Robot Navigation Algorithms

The paper presents a RGB-D dataset for development and evaluation of mobile robot navigation systems. The dataset was registered using a WiFiBot robot equipped with a Kinect sensor. Unlike the presently available datasets, the environment was specifically designed for the registration with the Kinect sensor. Moreover, it was ensured that the registered data is synchronized with the ground truth position of the robot. The presented dataset will be made publicly available for research purposes.

Improving accuracy of feature-based RGB-D SLAM by modeling spatial uncertainty of point features

Many recent solutions to the RGB-D SLAM problem use the pose-graph optimization approach, which marginalizes out the actual depth measurements. In this paper we employ the same type of factor graph optimization, but we investigate the gains coming from maintaining a map of RGBD point features and modeling the spatial uncertainty of these features. We demonstrate that RGB-D SLAM accuracy can be increased by employing uncertainty models reflecting the actual errors introduced by measurements and image processing. The new approach is validated in simulations and in experiments involving publicly available data sets to ensure that our results are verifiable.

Lightweight RGB-D SLAM System for Search and Rescue Robots

Search and rescue robots ought to be autonomous, as it enables to keep the human personnel out of dangerous areas. To achieve desirable level of the autonomy both environment mapping and reliable self-localization have to be implemented. In this paper we analyse the application of a fast, lightweight RGB-D Simultaneous Localization and Mapping (SLAM) system for robots involved in indoor/outdoor search and rescue missions. We demonstrate that under some conditions the RGB-D sensors provide data reliable enough even for outdoor, real-time SLAM. Experiments are performed on a legged robot and a wheeled robot, using two representative RGB-D sensors: the Asus Xtion Pro Live and the recently introduced Microsoft Kinect ver. 2.

Adopting Feature-Based Visual Odometry for Resource-Constrained Mobile Devices

In many practical applications of mobile devices self-localization of the user in a GPS-denied indoor environment is required. Among the available approaches the visual odometry concept enables continuous, precise egomotion estimation in previously unknown environments. In this paper we examine the usual pipeline of a monocular visual odometry system, identifying the bottlenecks and demonstrating how to circumvent the resource constrains, to implement a real-time visual odometry system on a smartphone or tablet.

Reactive posture behaviors for stable legged locomotion over steep inclines and large obstacles

Multi-legged walking robots often make use of sophisticated control architectures to play their strengths in rough and unknown environments. The adaptability of these robots is an essential skill to achieve the maneuverability and autonomy needed in their application fields. In this work we present a reactive control approach for the hexapod LAURONV, which enables it to overcome large obstacles and steep slopes without any knowledge about the environment. A key to this success can also be seen in the increased kinematic adaptability due to the fourth rotational joint in the bio-inspired leg kinematics. An extended experimental evaluation shows that the reactive posture behaviors are able to create an effective and efficient locomotion in challenging environments.

Indoor Navigation with a Smartphone Fusing Inertial and WiFi Data via Factor Graph Optimization

Mobile devices are getting more capable every year, allowing a variety of new applications, such like supporting pedestrian navigation in GPS-denied environments. In this paper we deal with the problem of combining in real-time dead reckoning data from the inertial sensors of a smartphone, and the WiFi signal fingerprints, which enable to detect the already visited places and therefore to correct the user’s trajectory. While both these techniques have been used before for indoor navigation with smartphones, the key contribution is the new method for including the localization constraints stemming from the highly uncertain WiFi fingerprints into a graphical problem representation (factor graph), which is then optimized in real-time on the smartphone. This method results in an Android-based personal navigation system that works robustly with only few locations of the WiFi access points known in advance, avoiding the need to survey WiFi signal in the whole area. The presented approach has been evaluated in public buildings, achieving localization accuracy which is sufficient for both pedestrian navigation and location-aware applications on a smartphone.