Gallery CV Research Publications About

Monika Roznere

Roboticist and Computer Scientist

Research Projects

3-D reconstruction using monocular camera and lights: multi-view photometric stereo for non-stationary robots


Abstract: This paper proposes a novel underwater Multi-View Photometric Stereo (MVPS) framework for reconstructing scenes in 3-D with a non-stationary low-cost robot equipped with a monocular camera and fixed lights. The underwater realm is the primary focus of study here, due to the challenges in utilizing underwater camera imagery and lack of low-cost reliable localization systems. Previous underwater PS approaches provided accurate scene reconstruction results, but assumed that the robot was stationary at the bottom. This assumption is limiting, as many artifacts, reefs, and man-made structures are large and meters above the bottom. Our proposed MVPS framework relaxes the stationarity assumption by utilizing a monocular SLAM system to estimate small robot motions and extract an initial sparse feature map. To compensate for the scale inconsistency in monocular SLAM output, our MVPS optimization scheme collectively estimates a high-quality, dense 3-D reconstruction and corrects the camera pose estimates. We also present an attenuation and camera-light extrinsic parameter calibration method for non-stationary robots. Finally, validation experiments with a BlueROV2 demonstrated the low-cost capability of producing high-quality scene reconstructions. Overall, this work is the foundation of an active perception pipeline for robots (i.e., underwater, ground, and aerial) to explore and map complex structures in high accuracy and resolution with an inexpensive sensor-light configuration.

[paper link] (published in ICRA 2023)

Memories ☺ : 4am 'night' dives; secret Mexican cenotes; peanut butter and seaweed smoothies.

Underwater monocular image depth estimation using single-beam echosounder


Abstract: This paper proposes a methodology for real-time depth estimation of underwater monocular camera images, fusing measurements from a single-beam echosounder. Our system exploits the echosounder's detection cone to match its measurements with the detected feature points from a monocular SLAM system. Such measurements are integrated in a monocular SLAM system to adjust the visible map points and the scale. We also provide a novel calibration process to determine the extrinsic between camera and echosounder to have reliable matching. Our proposed approach is implemented within ORB-SLAM2 and evaluated in a swimming pool and in the ocean to validate image depth estimation improvement. In addition, we demonstrate its applicability for improved underwater color correction. Overall, the proposed sensor fusion system enables inexpensive underwater robots with a monocular camera and echosounder to correct the depth estimation and scale in visual SLAM, leading to interesting future applications, such as underwater exploration and mapping.

[paper link] (published in IROS 2020)

Memories ☺ : reminder that I'm here for fun; lab aqaurium filled with snails and shrimp; undergrad computer networks class (took it to be with friends) suprisingly came in handy.

Towards a reliable heterogeneous robotic water quality monitoring system: An experimental analysis


Abstract: This paper describes experiments that tested the effect of robotic movement on the reliability of aquatic sensor readings. It also demonstrates the utility of a heterogeneous system of robots to advance limnological monitoring and research. An Autonomous Surface Vehicle (ASV) and an underwater Remotely Operated Vehicle (ROV), both equipped with multiparameter water quality sondes, were deployed weekly in Lake Sunapee, NH, to collect routine measurements horizontally over the water surface and vertically in the water column, respectively. We then compared the robot-collected data with data from fixed underwater instrument platforms (buoys) outfitted with a complementary suite of sensors as well as manually collected samples. The ASV was also deployed on one date in China Lake, ME, to test robotic procedures and evaluate potential environmental effects, including heterogeneity in water quality. We found that sensor response time and robotic movement (e.g., turns, stops, collisions) produced small discrepancies between the robot-derived and other datasets. Further, robotic coverage patterns impacted water quality parameter measurements, affecting our understanding of horizontal heterogeneity in biological and chemical data across the lake surface.

[paper link] (published in ISER 2020)

Memories ☺ : weekly naps on kayaks; thai sandwiches; sunburns; catching dramatic drones that wish to land on water.

Real-time model-based image color correction for underwater robots


Abstract: Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.

[paper link] (published in IROS 2019)

Memories ☺ : first field robotics trip; first time diving in the ocean; first time seeing a wild octopus (a lifetime dream of mine).