Online Multi Camera-IMU Calibration

Visual-inertial navigation systems are powerful in their ability to accurately estimate localization of mobile systems within complex environments that preclude the use of global navigation satellite systems. However, these navigation systems are reliant on accurate and up-to-date temporospatial calibrations of the sensors being used. As such, online estimators for these parameters are useful in resilient systems. This paper presents an extension to existing Kalman Filter based frameworks for estimating and calibrating the extrinsic parameters of multi-camera IMU systems. In addition to extending the filter framework to include multiple camera sensors, the measurement model was reformulated to make use of measurement data that is typically made available in fiducial detection software. A secondary filter layer was used to estimate time translation parameters without closed-loop feedback of sensor data. Experimental calibration results, including the use of cameras with non-overlapping fields of view, were used to validate the stability and accuracy of the filter formulation when compared to offline methods. Finally the generalized filter code has been open-sourced and is available online

Vehicular Teamwork: Collaborative localization of Autonomous Vehicles

This paper develops a distributed collaborative localization algorithm based on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB) measurements for vehicle to vehicle ranging, and shows improvements in localization accuracy where GPS typically falls short. The algorithm was first tested in a newly created open-source simulation environment that emulates various numbers of vehicles and sensors while simultaneously testing multiple localization algorithms. Predicted error distributions for various algorithms are quickly producible using the Monte-Carlo method and optimization techniques within MatLab. The simulation results were validated experimentally in an outdoor, urban environment. Improvements of localization accuracy over a typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test runs. When GPS was denied, these improvements increased up to 83.3% over a standard kalman filter. In both simulation and experimentally, the DCL algorithm was shown to be a good approximation of a full state filter, while reducing required communication between vehicles. These results are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GPS-denied environments. In the future, additional moving vehicles with additional tags will be tested in other challenging GPS denied environments

AutoCone: An OmniDirectional Robot for Lane-Level Cone Placement

This paper summarizes the progress in developing a rugged, low-cost, automated ground cone robot network capable of traffic delineation at lane-level precision. A holonomic omnidirectional base with a traffic delineator was developed to allow flexibility in initialization. RTK GPS was utilized to reduce minimum position error to 2 centimeters. Due to recent developments, the cost of the platform is now less than $1,600. To minimize the effects of GPS-denied environments, wheel encoders and an Extended Kalman Filter were implemented to maintain lane-level accuracy during operation and a maximum error of 1.97 meters through 50 meters with little to no GPS signal. Future work includes increasing the operational speed of the platforms, incorporating lanelet information for path planning, and cross-platform estimation

Radar-Only Off-Road Local Navigation

Off-road robotics have traditionally utilized lidar for local navigation due to its accuracy and high resolution. However, the limitations of lidar, such as reduced performance in harsh environmental conditions and limited range, have prompted the exploration of alternative sensing technologies. This paper investigates the potential of radar for off-road local navigation, as it offers the advantages of a longer range and the ability to penetrate dust and light vegetation. We adapt existing lidar-based methods for radar and evaluate the performance in comparison to lidar under various off-road conditions. We show that radar can provide a significant range advantage over lidar while maintaining accuracy for both ground plane estimation and obstacle detection. And finally, we demonstrate successful autonomous navigation at a speed of 2.5 m/s over a path length of 350 m using only radar for ground plane estimation and obstacle detection.Comment: 7 pages, 17 figures, ITSC 202

Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle

We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.)

RELLIS-3D Dataset: Data, Benchmarks and Analysis

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal dataset collected in an off-road environment, which contains annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University, and presents challenges to existing algorithms related to class imbalance and environmental topography. Additionally, we evaluate the current state of the art deep learning semantic segmentation models on this dataset. Experimental results show that RELLIS-3D presents challenges for algorithms designed for segmentation in urban environments. This novel dataset provides the resources needed by researchers to continue to develop more advanced algorithms and investigate new research directions to enhance autonomous navigation in off-road environments. RELLIS-3D will be published at https://github.com/unmannedlab/RELLIS-3D