Online Multi-Camera and IMU Calibration

This research focuses on the implementation of an online multi-camera IMU calibration filter that is based on the work of Mirzaei and Roumeliotis. This work expands on what has been previously done by incorporating the fiducial marker detectors provided by OpenCV and manipulating the update equations to utilize the quaternion measurement provided by these detectors. The main challenge was in developing the Jacobians for the quaternion measurement, such that the updates would be stable even... 2 minute read

Roadside LiDAR Dataset

A roadside LiDAR dataset both in urban and in highway environments with annotated vehicles. This dataset corresponds to the dataset described in “Building a Smart Work Zone Using Roadside LiDAR”. Our dataset contains two ~10 minute segments on a urban (45 mph) and highway segment (75 mph), consisting of >1000 frames labeled of measurements taken with a Velodyne VLP-16. The dataset format is as follows: Raw (unprocessed ROI filtering) rosbags Bird’s Eye View projection images... 1 minute read

SemanticUSL: A Dataset for LiDAR Semantic Segmentation Domain Adaptation

SemanticUSL was collected on a Clearpath Warthog robotics with an Ouster OS1-64 Lidar. The data collection location includes the campus site and off-road research facility of Texas A& M University. The data include the traffic-road scene, walk-road scene, and off-road scene. Our dataset has 16578 unlabeled scans for domain adaptation training and 1200 labeled scans for evaluation. The data uses the same format and ontology as SemanticKITTI; therefore, it can be easily used for domain... 1 minute read

RELLIS-3D: A Multi-modal Dataset for Off-Road Robotics

     Peng Jiang1, Philip Osteen2, Maggie Wigness2 and Srikanth Saripalli1 1. Texas A&M University;  2. CCDC Army Research Laboratory [Website] [Paper] [Github] Overview 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 represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal... 4 minute read

LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

Introduction We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two branch structure. We embedded Gated-SCNN into the segmenter component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the... 4 minute read

Ultra-Wideband Localization

Introduction There are numerous industries that are in the process of developing autonomous passenger vehicles. Such vehicles have the capacity to reduce risk and free time of riders. In the development of an autonomous vehicles, a key consideration is reliable localization in order to plan for and react to the environment. Typically,h igh position accuracy is achieved through the use of Global Navigation Satellite Systems (GNSS). The use of GNSS with an Inertial Measurement Unit... 8 minute read

Improving Occlusion Occupancy Prediction

Safe planning for autonomous vehicles requires accurate prediction of future vehicle motion. For observable vehicles, future motion is limited by physical constraints as well as some logical (rules of the road) constraints. For sensor occlusion, the assumption has been that a vehicle may exit at any time, at a speed up to and slightly exceeding the speed limit. This approach does not consider the passage of time, or any spatial perception. Our research focuses on... 1 minute read

Extrinsic Calibration of Multiple Sensors

Cross calibration of multiple sensors is a classic estimation problem in robotics and with the advent of multi-sensor perception and state estimation techniques, this problem has gained paramount importance. We at USL are working on both target-based and targetless approaches of multi sensor calibration. Having already studied and implemented a few targetbased approaches we are currently exploring targetless aproaches which can be used when the robot is operating in real-time. The long term goal is... 1 minute read

Teleoperation for Autonomous Shuttles

Our research is investigating shared driving operations between autonomy, and a remote operator. In challenging urban scenarios, such as traffic jams and accidents, the autonomy may require human intervention. Currently, teleoperation for our shuttles is manual control via four separate 4G LTE connections. Current research goals focus on reducing teleoperator stress by exploring different methods of remote vehicle guidance and control for ground vehicles in city environments.

Autonomous Cone Placement

The Auto Cone project is an effort to develop cones that are capable of localizing and placing themselves to improve safety conditions for highway workers. These cones utilize RTK GPS and onboard localization filtering to produce decimeter-level accuracy in placing themselves in road conditions. Additionally, they are capable of transitioning through GPS-denied environments such as under bridges or overpasses. Pictured are two of the cones and the real-time kinematic (RTK) base station. Problem: The goal... 2 minute read

Autonomous Shuttle

We have a drive-by-wire electric shuttle equipped with several basic sensors needed for autonomous operation. The shuttle is capable of following a predetermined GPS waypoint path at speeds of around 10 kph. Roof-mounted 3D LIDAR is used for obstacle detection, and a forward-facing camera is used for object classification. We believe that Autonomous Shuttles can solve the last mile problem effectively and cheaply. Towards this end, we plan on deploying the first shuttle on Texas... 2 minute read

Autonomous Trucking

We utilize a modified International Prostar 122+ from AutonomouStuff for single L4 automations research. Current investigations include systems integration, simulation, and control. For simulation, we have utilized the American Truck Simulator (ATS) video game, with a custom telemetry plugin to integrate vehicle data with ROS.

Past Research Projects