1 code implementation • 19 Mar 2024 • Kasi Viswanath, Peng Jiang, Srikanth Saripalli
Additionally, we also investigate the possible benefits of using calibrated intensity in semantic segmentation in urban environments (SemanticKITTI) and cross-sensor domain adaptation.
no code implementations • 17 Mar 2024 • Peng Jiang, Gaurav Pandey, Srikanth Saripalli
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting.
1 code implementation • 2 Jan 2024 • Kasi Viswanath, Peng Jiang, Sujit PB, Srikanth Saripalli
LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning.
no code implementations • 24 Nov 2023 • Jia Huang, Peng Jiang, Alvika Gautam, Srikanth Saripalli
To our knowledge, this research is the first to conduct both quantitative and qualitative evaluations of VLMs in the context of pedestrian behavior prediction for autonomous driving.
no code implementations • 20 Oct 2023 • Peng Jiang, Srikanth Saripalli
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential.
no code implementations • 22 May 2023 • Jia Huang, Alvika Gautam, Srikanth Saripalli
Evaluation results illustrate that the proposed method reaches an accuracy of 81% on action prediction task on JAAD testing data and outperforms the LSTM-ed by 7. 4%, while LSTM counterpart performs much better on trajectory prediction task for a prediction sequence length of 25 frames.
no code implementations • 17 May 2023 • Peng Jiang, Srikanth Saripalli
This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems.
no code implementations • 26 Sep 2022 • Kasi Vishwanath, P. B. Sujit, Srikanth Saripalli
In this paper, we address the problem of learning the cost-map values from the sensed environment for robust vehicle path planning.
no code implementations • 24 Jun 2022 • Peng Jiang, Srikanth Saripalli
Moreover, we conduct experiments on a real-world dataset to show the effectiveness of our loss function and network structure.
no code implementations • 5 Oct 2021 • Akhil Nagariya, Dileep Kalathil, Srikanth Saripalli
Compared to the standard ILQR approach, our proposed approach achieves a 30% and 50% reduction in cross track error in Warthog and Moose, respectively, by utilizing only 30 minutes of real-world driving data.
no code implementations • 21 Sep 2021 • Peng Jiang, Philip Osteen, Srikanth Saripalli
This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information.
no code implementations • 24 Apr 2021 • Peng Jiang, Philip Osteen, Srikanth Saripalli
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information.
1 code implementation • 23 Mar 2021 • Kasi Viswanath, Kartikeya Singh, Peng Jiang, Sujit P. B., Srikanth Saripalli
Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures.
3 code implementations • 17 Nov 2020 • Peng Jiang, Philip Osteen, Maggie Wigness, Srikanth Saripalli
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.
Ranked #1 on 3D Semantic Segmentation on RELLIS-3D Dataset
no code implementations • 28 Jul 2020 • Akhil Nagariya, Srikanth Saripalli
We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM
no code implementations • 2 Mar 2020 • Peng Jiang, Srikanth Saripalli
We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet).
no code implementations • 18 Oct 2019 • Timothy Overbye, Srikanth Saripalli
Then the most optimal trajectory, as determined by the cost map and proximity to A* path, is chosen and sent to the controller.
no code implementations • 7 Apr 2018 • Sai Vemprala, Srikanth Saripalli
This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments.
Robotics