Search Results for author: Srikanth Saripalli

Found 18 papers, 4 papers with code

Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms

no code implementations7 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

Fast Local Planning and Mapping in Unknown Off-Road Terrain

no code implementations18 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.

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

no code implementations2 Mar 2020 Peng Jiang, Srikanth Saripalli

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet).

Domain Adaptation Segmentation +1

An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model

no code implementations28 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

Model Predictive Control

RELLIS-3D Dataset: Data, Benchmarks and Analysis

3 code implementations17 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.

3D Semantic Segmentation Autonomous Navigation +2

OFFSEG: A Semantic Segmentation Framework For Off-Road Driving

1 code implementation23 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.

Scene Understanding Segmentation +1

Calibrating LiDAR and Camera using Semantic Mutual information

no code implementations24 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.

Image Registration

SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator

no code implementations21 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.

Camera Calibration Image Registration

OTTR: Off-Road Trajectory Tracking using Reinforcement Learning

no code implementations5 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.

reinforcement-learning Reinforcement Learning (RL)

Contrastive Learning of Features between Images and LiDAR

no code implementations24 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.

Contrastive Learning

CAMEL: Learning Cost-maps Made Easy for Off-road Driving

no code implementations26 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.

Improving Extrinsics between RADAR and LIDAR using Learning

no code implementations17 May 2023 Peng Jiang, Srikanth Saripalli

This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems.

Autonomous Driving Sensor Fusion

Learning Pedestrian Actions to Ensure Safe Autonomous Driving

no code implementations22 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.

Autonomous Driving Trajectory Prediction

ROSS: Radar Off-road Semantic Segmentation

no code implementations20 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.

Autonomous Navigation Segmentation +1

GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction

no code implementations24 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.

Autonomous Driving Common Sense Reasoning +3

Off-Road LiDAR Intensity Based Semantic Segmentation

1 code implementation2 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.

Autonomous Driving LIDAR Semantic Segmentation +2

3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization

no code implementations17 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.

Visual Localization

Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation

1 code implementation19 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.

Domain Adaptation LIDAR Semantic Segmentation +2

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