Ankit Laddha — accepted AI-conference papers

2021

LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting

Gregory P. Meyer, Jake Charland, Shreyash Pandey, Ankit Laddha, Shivam Gautam, Carlos Vallespi-Gonzalez +1

RA-L 2021

In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR. Unlike the previous work, our approach utilizes the native range view representation of the LiDAR, which enables our method to operate at the full range of the sensor in real-time witho

Cited by 46 Source ↗Scholar ↗

2021

RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting

Ankit Laddha, Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington

IROS 2021poster

Robust real-time detection and motion forecasting of traffic participants is necessary for autonomous vehicles to safely navigate urban environments. In this paper, we present RV-FuseNet, a novel end-to-end approach for joint detection and trajectory estimation directly from time-series LiDAR data.…

Cited by 19 Source ↗Scholar ↗

2020

LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion

Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, sida zhang +2

CoRL 2020

In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and HD maps. Automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous radial

Cited by 0 Source ↗PDF ↗Scholar ↗

2019

LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving

Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington

CVPR 2019poster

In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view invo…

Cited by 440 PDF ↗Scholar ↗

2015

Active Learning for Structured Probabilistic Models With Histogram Approximation

Qing Sun, Ankit Laddha, Dhruv Batra

CVPR 2015poster

Abstract. This paper studies active learning in structured probabilistic models such as Conditional Random Fields (CRFs). This is a challenging problem because unlike unstructured prediction problems such as binary or multi-class classification, structured prediction problems involve a distribution…

Cited by 32 Source ↗PDF ↗Scholar ↗