Tapered whisker reservoir computing for real-time terrain identification-based navigation

This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain

Pyörivien monilaserkeilainjärjestelmien geometrinen kalibrointi

The introduction of light-weight and low-cost multi-beam laser scanners provides ample opportunities in positioning and mapping as well as automation and robotics. The fields of view (FOV) of these sensors can be further expanded by actuation, for example by rotation. These rotating multi-beam lidar (RMBL) systems can provide fast and expansive coverage of the geometries of spaces, but the nature of the sensors and their actuation leave room for improvement in accuracy and precision. Geometric calibration methods addressing this space have been proposed, and this thesis reviews a selection of these methods and evaluates their performance when applied to a set of data samples collected using a custom RMBL platform and six Velodyne multi-beam sensors (one VLP-16 Lite, four VLP-16s and one VLP-32C). The calibration algorithms under inspection are unsupervised and data-based, and they are quantitatively compared to a target-based calibration performed using a high-accuracy point cloud obtained using a terrestrial laser scanner as a reference. The data-based calibration methods are automatic plane detection and fitting, a method based on local planarity and a method based on the information-theoretic concept of information entropy. It is found that of these, the plane-fitting and entropy-based measures for point cloud quality obtain the best calibration results.Kevyet ja edulliset monilaserkeilaimet tuovat uusia mahdollisuuksia paikannus- ja kartoitusaloille mutta myös automaatioon ja robotiikkaan. Näiden sensorien näköaloja voidaan kasvattaa entisestään esimerkiksi pyörittämällä, ja näin toteutettavat pyörivät monilaserkeilainjärjestelmät tuottavat nopeasti kattavaa geometriaa niitä ympäröivistä tiloista. Sensorien rakenne ja järjestelmän liikkuvuus lisäävät kuitenkin kohinaa ja epävarmuutta mittauksissa, minkä vuoksi erilaisia geometrisia kalibrointimenetelmiä onkin ehdotettu aiemmassa tutkimuksessa. Tässä diplomityössä esitellään valikoituja kalibrointimenetelmiä ja arvioidaan niiden tuloksia koeasetelmassa, jossa pyörivälle alustalle asennetuilla Velodyne-monilaserkeilaimilla (yksi VLP-16 Lite, neljä VLP-16:aa ja yksi VLP-32C) mitataan liikuntasalin geometriaa. Tarkasteltavat menetelmät ovat valvomattomia ja vain mittauksiin perustuvia ja niitä verrataan samasta tilasta hankittuun tarkkaan maalaserkeilausaineistoon. Menetelmiä ovat tasojen automaattinen etsintä ja sovitus, paikalliseen tasomaisuuteen perustuva menetelmä sekä informaatioteoreettiseen entropiaan perustuva menetelmä. Näistä tasojen sovitus ja entropiamenetelmä saavuttivat parhaat kalibrointitulokset referenssikalibraatioon verrattaessa

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE