Josip Ćesić

Smart home automation system for energy efficient housing

This paper presents a concept and implementation of modern smart monitoring and control system for building automatization. The system is designed to enable significant reduction of energy consumption and carbon footprint by increasing the energy efficiency of the building under control. The system consists of a Linux-based remotely accessible main embedded control unit, a custom designed programmable logic controller named littlePLC, and a propriatery low-power Wireless Sensor Network (WSN). The energy flow is optimized by using a Model Predictive Control (MPC) algorithm that runs on the main control unit. The main control unit communicates with littlePLC, which serves as an interface that controls the parameters and state of HVAC systems in the building. The feedback information for MPC is gathered by means of the WSN, which consists of various sensor node types, such as temperature, air pressure, humidity, VOC and CO2. The WSN nodes are connected in a star type network topology, with a communication HUB connected to the main control unit. The information gathered by WSN are used in the MPC algorithm in order to calculate and estimate the requirements for heat corrections, with respect to ventilation and weather predictions.

Radar and stereo vision fusion for multitarget tracking on the special Euclidean group

Reliable scene analysis, under varying conditions, is an essential task in nearly any assistance or autonomous system application, and advanced driver assistance systems (ADAS) are no exception. ADAS commonly involve adaptive cruise control, collision avoidance, lane change assistance, traffic sign recognition, and parking assistance-with the ultimate goal of producing a fully autonomous vehicle. The present paper addresses detection and tracking of moving objects within the context of ADAS. We use a multisensor setup consisting of a radar and a stereo camera mounted on top of a vehicle. We propose to model the sensors uncertainty in polar coordinates on Lie Groups and perform the objects state filtering on Lie groups, specifically, on the product of two special Euclidean groups, i.e., SE ( 2 ) 2 . To this end, we derive the designed filter within the framework of the extended Kalman filter on Lie groups. We assert that the proposed approach results with more accurate uncertainty modeling, since used sensors exhibit contrasting measurement uncertainty characteristics and the predicted target motions result with banana-shaped uncertainty contours. We believe that accurate uncertainty modeling is an important ADAS topic, especially when safety applications are concerned. To solve the multitarget tracking problem, we use the joint integrated probabilistic data association filter and present necessary modifications in order to use it on Lie groups. The proposed approach is tested on a real-world dataset collected with the described multisensor setup in urban traffic scenarios. Radar and stereo camera integration for tracking in ADAS.Detection and tracking of moving objects by filtering on matrix Lie groups.State space formed as a product of two special Euclidean groups.Employed banana-shaped uncertainties typical for range-bearing sensors and vehicles in motion.JIPDA filter for multitarget tracking on matrix Lie groups.

Spincopter Wing Design and Flight Control

This paper presents dynamical properties of an unmanned aerial vehicle (UAV), called spincopter. The vehicle structure is based on two wings that are forced in rotation (spinning) by propulsion system formed of two propellers. Based on devised dynamical model, that reveals inherent stability of the vehicle, composition of control algorithms for vertical and horizontal movement is proposed. Due to the specific configuration of the propulsion system, movement in horizontal direction is produced by pulsations in rotational speed of propulsion motors. An analysis of influence that such a configuration has on the vehicle dynamics is given. Finally, design recommendations for rotational wings are elaborated, based on extensive simulations of spincopter by using X-Plane® software package.

Von Mises Mixture PHD Filter

This letter deals with the problem of tracking multiple targets on the unit circle, a problem that arises whenever the state and the sensor measurements are circular, i.e. angular-only, random variables. To tackle this problem, we propose a novel mixture approximation of the probability hypothesis density filter based on the von Mises distribution, thus constructing a method that globally captures the non-Euclidean nature of the state and the measurement space. We derive a closed-form recursion of the filter and apply principled approximations where necessary. We compared the performance of the proposed filter with the Gaussian mixture probability hypothesis density filter on a synthetic dataset of 100 randomly generated multitarget trajectory examples corrupted with noise and clutter, and on the PETS2009 dataset. We achieved respectively a decrease of 10.5% and 2.8% in the optimal subpattern assignment metric (notably 16.9% and 10.8% in the localization component).

Full body human motion estimation on lie groups using 3D marker position measurements

This paper proposes a new algorithm for full body human motion estimation using 3D marker position measurements. The joints are represented with Lie group members, including special orthogonal groups SO(2) and SO(3), and a special euclidean group SE(3). We employ the Lie Group Extended Kalman Filter (LG-EKF) for stochastic inference on groups, thus explicitly accounting for the non-euclidean geometry of the state space, and provide the derivation of the LG-EKF recursion for articulated motion estimation. We evaluate the performance of the proposed algorithm in both simulation and on real-world motion capture data, comparing it with the Euler angles based EKF. The results show that the proposed filter significantly outperforms the Euler angles based EKF, since it estimates human motion more accurately and is not affected by gimbal lock.

Extended information filter on matrix Lie groups

In this paper we propose a new state estimation algorithm called the extended information filter on Lie groups. The proposed filter is inspired by the extended Kalman filter on Lie groups and exhibits the advantages of the information filter with regard to multisensor update and decentralization, while keeping the accuracy of stochastic inference on Lie groups. We present the theoretical development and demonstrate its performance on multisensor rigid body attitude tracking by forming the state space on the SO(3)×R3 group, where the first and second component represent the orientation and angular rates, respectively. The performance of the filter is compared with respect to the accuracy of attitude tracking with parametrization based on Euler angles and with respect to execution time of the extended Kalman filter formulation on Lie groups. The results show that the filter achieves higher performance consistency and smaller error by tracking the state directly on the Lie group and that it keeps smaller computational complexity of the information form with respect to high number of measurements.

Appearance Based Vehicle Detection by Radar-Stereo Vision Integration

This paper proposes a novel method for appearance based vehicle detection by employing stereo vision system and radar units. In the vein of utilizing advanced driver assistance systems, detection and tracking of moving objects or particularly vehicles, represents an essential task. For the merits of such application, it has often been suggested to combine multiple sensors with complementary modalities. In accordance, in this work we utilize a stereo vision and two radar units, and fuse the corresponding modalities at the level of detection. Firstly, the algorithm executes the detection procedure based on stereo image solely, generating the information about vehicles’ position. Secondly, the final unique list of vehicles is obtained by overlapping the radar readings with the preliminary list obtained by stereo system. The stereo vision–based detection procedure consists of (i) edge processing plugging in also the information about disparity map, (ii) shape based vehicles’ contour extraction and (iii) preliminary vehicles’ positions generation. Since the radar readings are examined by overlapping them with the list obtained by stereo vision, the proposed algorithm can be considered as high level fusion approach. We analyze the performance of the proposed algorithm by performing the real-world experiment in highly dynamic urban environment, under significant illumination influences caused by sunny weather.

Short-Term Map Based Detection and Tracking of Moving Objects with 3D Laser on a Vehicle

Detection and tracking of moving objects is an essential problem in situational awareness context and hence crucial for many robotic applications. Here we propose a method for the detection of moving objects with a 3D laser range sensor and a variation of the method for tracking multiple detected objects. The detection procedure starts with the ground extraction using random sample consensus approach for model parameter estimation. The resulting point cloud is then downsampled using voxel grid approach and filtered using a radius outlier rejection method. Within the approach, we have utilized a procedure for building short-term maps of the environment by using the octree data structure. This data structure enables an efficient comparison of the current scan and the short-term local map, thus detecting dynamic parts of scene. The ego-motion of the mobile platform is compensated using the available odometry information, which is rather imperfect, and hence is refined using the iterative closest point registration technique. Furthermore, due to sensor characteristics, the iterative closest point is carried out in 2D between the short-term map and the current, where the non-ground filtered scans are projected onto 2D. The tracking task is based on the joint probabilistic data association filter and Kalman filtering with variable process and measurement noise which take into account velocity and position of the tracked objects. Since this data association approach assumes a constant and known number of objects, we have utilized a specific entropy based track management. The experiments performed using Velodyne HDL-32E laser sensor mounted on top of a mobile platform demonstrate the suitability and efficiency of the proposed method.

Moving Objects Tracking on the Unit Sphere Using a Multiple-Camera System on a Mobile Robot

Detection and tracking of moving objects with camera systems mounted on a mobile robot presents a formidable problem since the ego-motion of the robot and the moving objects jointly form a challengingly discernible motion in the image. In this paper, we are concerned with multiple-camera systems, namely the Ladybug\(^{\textregistered }2\) camera, whose perspective images were used to detect motion and subsequently perform the tracking of multiple objects on the sphere. This enabled us to account for the continuity of the scene which is achieved by the sensor in an image stitching process on the sphere. The objects are tracked on the sphere with a Bayesian filter based on the von Mises–Fisher distribution and the data association is achieved by the global nearest neighbor method, for which the distance matrix is constructed by deriving the Renyi \(\alpha \)-divergence for the von Mises–Fisher distribution. The prospects of the method are tested on a synthetic and real-world data experiments.

Exactly Sparse Delayed State Filter on Lie groups for Long-term Pose Graph SLAM

In this paper we propose a simultaneous localization and mapping (SLAM) back-end solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive LG-ESDSF and demonstrate that it retains all the good characteristics of the classic Euclidean ESDSF, the main advantage being the exact sparsity of the information matrix. The key advantage of LG-ESDSF in comparison with the classic ESDSF lies in the ability to respect the state space geometry by negotiating uncertainties and employing filtering equations directly on Lie groups. We also exploit the special structure of the information matrix in order to allow long-term operation while the robot is moving repeatedly through the same environment. To prove the effectiveness of the proposed SLAM solution, we conducted extensive experiments on two different publicly available datasets, namely the KITTI and EuRoC datasets, using two front-ends: one based on the stereo camera and the other on the 3D LIDAR. We compare LG-ESDSF with the general graph optimization framework ( g 2 o ) when coupled with the same front-ends. Similarly to g 2 o the proposed LG-ESDSF is front-end agnostic and the comparison demonstrates that our solution can match the accuracy of g 2 o , while maintaining faster computation times. Furthermore, the proposed back-end coupled with the stereo camera front-end forms a complete visual SLAM solution dubbed LG-SLAM. Finally, we evaluated LG-SLAM using the online KITTI protocol and at the time of writing it achieved the second best result among the stereo odometry solutions and the best result among the tested SLAM algorithms.