An Effective Multi-Cue Positioning System for Agricultural Robotics

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters, 201

Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.Comment: Accepted by ICRA 201

Monocular Visual Odometry for Fixed-Wing Small Unmanned Aircraft Systems

The popularity of small unmanned aircraft systems (SUAS) has exploded in recent years and seen increasing use in both commercial and military sectors. A key interest area for the military is to develop autonomous capabilities for these systems, of which navigation is a fundamental problem. Current navigation solutions suffer from a heavy reliance on a Global Positioning System (GPS). This dependency presents a significant limitation for military applications since many operations are conducted in environments where GPS signals are degraded or actively denied. Therefore, alternative navigation solutions without GPS must be developed and visual methods are one of the most promising approaches. A current visual navigation limitation is that much of the research has focused on developing and applying these algorithms on ground-based vehicles, small hand-held devices or multi-rotor SUAS. However, the Air Force has a need for fixed-wing SUAS to conduct extended operations. This research evaluates current state-of-the-art, open-source monocular visual odometry (VO) algorithms applied on fixed-wing SUAS flying at high altitudes under fast translation and rotation speeds. The algorithms tested are Semi-Direct VO (SVO), Direct Sparse Odometry (DSO), and ORB-SLAM2 (with loop closures disabled). Each algorithm is evaluated on a fixed-wing SUAS in simulation and real-world flight tests over Camp Atterbury, Indiana. Through these tests, ORB-SLAM2 is found to be the most robust and flexible algorithm under a variety of test conditions. However, all algorithms experience great difficulty maintaining localization in the collected real-world datasets, showing the limitations of using visual methods as the sole solution. Further study and development is required to fuse VO products with additional measurements to form a complete autonomous navigation solution

Robust Legged Robot State Estimation Using Factor Graph Optimization

Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position drift during dynamic motions such as trotting. To this end, we present a factor graph optimization method for state estimation which tightly fuses and smooths inertial navigation, leg odometry and visual odometry. The effectiveness of the approach is demonstrated using the ANYmal quadruped robot navigating in a realistic outdoor industrial environment. This experiment included trotting, walking, crossing obstacles and ascending a staircase. The proposed approach decreased the relative position error by up to 55% and absolute position error by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201

Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled)

The Newer College dataset: handheld LiDAR, inertial and vision with ground truth

In this paper, we present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km around New College, Oxford as well as a series of supplementary datasets with much more aggressive motion and lighting contrast. The datasets include data from two commercially available devices - a stereoscopic-inertial camera and a multi-beam 3D LiDAR, which also provides inertial measurements. Additionally, we used a tripod-mounted survey grade LiDAR scanner to capture a detailed millimeter-accurate 3D map of the test location (containing ~290 million points). Using the map, we generated a 6 Degrees of Freedom (DoF) ground truth pose for each LiDAR scan (with approximately 3 cm accuracy) to enable better benchmarking of LiDAR and vision localisation, mapping and reconstruction systems. This ground truth is the particular novel contribution of this dataset and we believe that it will enable systematic evaluation which many similar datasets have lacked. The large dataset combines both built environments, open spaces and vegetated areas so as to test localisation and mapping systems such as vision-based navigation, visual and LiDAR SLAM, 3D LiDAR reconstruction and appearance-based place recognition, while the supplementary datasets contain very dynamic motions to introduce more challenges for visual-inertial odometry systems. The datasets are available at:ori.ox.ac.uk/datasets/newer-college-dataset

Field-based measurement of hydrodynamics associated with engineered in-channel structures: the example of fish pass assessment

The construction of fish passes has been a longstanding measure to improve river ecosystem status by ensuring the passability of weirs, dams and other in- channel structures for migratory fish. Many fish passes have a low biological effectiveness because of unsuitable hydrodynamic conditions hindering fish to rapidly detect the pass entrance. There has been a need for techniques to quantify the hydrodynamics surrounding fish pass entrances in order to identify those passes that require enhancement and to improve the design of new passes. This PhD thesis presents the development of a methodology for the rapid, spatially continuous quantification of near-pass hydrodynamics in the field. The methodology involves moving-vessel Acoustic Doppler Current Profiler (ADCP) measurements in order to quantify the 3-dimensional water velocity distribution around fish pass entrances. The approach presented in this thesis is novel because it integrates a set of techniques to make ADCP data robust against errors associated with the environmental conditions near engineered in-channel structures. These techniques provide solutions to (i) ADCP compass errors from magnetic interference, (ii) bias in water velocity data caused by spatial flow heterogeneity, (iii) the accurate ADCP positioning in locales with constrained line of sight to navigation satellites, and (iv) the accurate and cost-effective sensor deployment following pre-defined sampling strategies. The effectiveness and transferability of the methodology were evaluated at three fish pass sites covering conditions of low, medium and high discharge. The methodology outputs enabled a detailed quantitative characterisation of the fish pass attraction flow and its interaction with other hydrodynamic features. The outputs are suitable to formulate novel indicators of hydrodynamic fish pass attractiveness and they revealed the need to refine traditional fish pass design guidelines