Manikandasriram Srinivasan Ramanagopal — accepted AI-conference papers

2023

CLONeR: Camera-Lidar Fusion for Occupancy Grid-Aided Neural Representations

Alexandra Carlson, Manikandasriram Srinivasan Ramanagopal, Nathan Tseng, Matthew Johnson-Roberson, Ram Vasudevan, Katherine A. Skinner

RA-L 2023

Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art novel view synthesis and facilitate dense estimation of scene properties. However, NeRFs often fail for outdoor, unbounded scenes that are captured under very sparse views with the scene content concentrated far away from the

Cited by 26 Source ↗Scholar ↗

2020

LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery

Junming Zhang, Manikandasriram Srinivasan Ramanagopal, Ram Vasudevan, Matthew Johnson-Roberson

ICRA 2020poster

An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces spa…

Cited by 42 Source ↗Scholar ↗

2020

Pixel-Wise Motion Deblurring of Thermal Videos

Manikandasriram Srinivasan Ramanagopal, Zixu Zhang, Ram Vasudevan, Matthew Johnson Roberson

RSS 2020poster

Uncooled microbolometers can enable robots to see in the absence of visible illumination by imaging the “heat” radiated from the scene. Despite this ability to see in the dark, these sensors suffer from significant motion blur. This has limited their application on robotic systems. As described in…

Cited by 18 Source ↗PDF ↗Scholar ↗

2019

PedX: Benchmark Dataset for Metric 3-D Pose Estimation of Pedestrians in Complex Urban Intersections

Wonhui Kim, Manikandasriram Srinivasan Ramanagopal, Charles Barto, Ming-Yuan Yu, Karl Rosaen, Nick Goumas +2

RA-L 2019

This letter presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5 000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing two-dimensional (2-D) image labels and 3-D labels of

Cited by 73 Source ↗Scholar ↗

2018

Failing to Learn: Autonomously Identifying Perception Failures for Self-Driving Cars

Manikandasriram Srinivasan Ramanagopal, Cyrus Anderson, Ram Vasudevan, Matthew Johnson-Roberson

RA-L 2018

One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical applicatio

Cited by 114 Source ↗Scholar ↗