MPVO: Motion-Prior based Visual Odometry for PointGoal Navigation
- URL: http://arxiv.org/abs/2411.04796v1
- Date: Thu, 07 Nov 2024 15:36:49 GMT
- Title: MPVO: Motion-Prior based Visual Odometry for PointGoal Navigation
- Authors: Sayan Paul, Ruddra dev Roychoudhury, Brojeshwar Bhowmick,
- Abstract summary: Visual odometry (VO) is essential for enabling accurate point-goal navigation of embodied agents in indoor environments.
Recent deep-learned VO methods show robust performance but suffer from sample inefficiency during training.
We propose a robust and sample-efficient VO pipeline based on motion priors available while an agent is navigating an environment.
- Score: 3.9974562667271507
- License:
- Abstract: Visual odometry (VO) is essential for enabling accurate point-goal navigation of embodied agents in indoor environments where GPS and compass sensors are unreliable and inaccurate. However, traditional VO methods face challenges in wide-baseline scenarios, where fast robot motions and low frames per second (FPS) during inference hinder their performance, leading to drift and catastrophic failures in point-goal navigation. Recent deep-learned VO methods show robust performance but suffer from sample inefficiency during training; hence, they require huge datasets and compute resources. So, we propose a robust and sample-efficient VO pipeline based on motion priors available while an agent is navigating an environment. It consists of a training-free action-prior based geometric VO module that estimates a coarse relative pose which is further consumed as a motion prior by a deep-learned VO model, which finally produces a fine relative pose to be used by the navigation policy. This strategy helps our pipeline achieve up to 2x sample efficiency during training and demonstrates superior accuracy and robustness in point-goal navigation tasks compared to state-of-the-art VO method(s). Realistic indoor environments of the Gibson dataset is used in the AI-Habitat simulator to evaluate the proposed approach using navigation metrics (like success/SPL) and pose metrics (like RPE/ATE). We hope this method further opens a direction of work where motion priors from various sources can be utilized to improve VO estimates and achieve better results in embodied navigation tasks.
Related papers
- Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied
Scenarios [66.05091704671503]
We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks.
We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
arXiv Detail & Related papers (2024-02-04T08:41:20Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - Unsupervised Visual Odometry and Action Integration for PointGoal
Navigation in Indoor Environment [14.363948775085534]
PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point.
To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner.
Experiments show that the proposed system achieves satisfactory results and outperforms the partially supervised learning algorithms on the popular Gibson dataset.
arXiv Detail & Related papers (2022-10-02T03:12:03Z) - StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy.
Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
arXiv Detail & Related papers (2022-07-21T12:03:02Z) - Real-time Object Detection for Streaming Perception [84.2559631820007]
Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception.
We build a simple and effective framework for streaming perception.
Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
arXiv Detail & Related papers (2022-03-23T11:33:27Z) - Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
We present an efficient and robust LiDAR-based end-to-end navigation framework.
We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design.
We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
arXiv Detail & Related papers (2021-05-20T17:52:37Z) - FAITH: Fast iterative half-plane focus of expansion estimation using
event-based optic flow [3.326320568999945]
This study proposes the FAst ITerative Half-plane (FAITH) method to determine the course of a micro air vehicle (MAV)
Results show that the computational efficiency of our solution outperforms state-of-the-art methods while keeping a high level of accuracy.
arXiv Detail & Related papers (2021-02-25T12:49:02Z) - Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D
Environments [11.657524999491029]
In this work, we used deep reinforcement learning combining Q-learning with a neural representation to avoid instability.
Our methodology uses deep q-learning and combines it with a rolling wave planning approach on agile methodology.
Experimental results show that the proposed method enhanced the performance of VVN by 55.31 on average for long-distance missions.
arXiv Detail & Related papers (2020-03-23T12:58:58Z) - MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale
Robotic Navigation [23.54696982881734]
We propose a novel motion and visual perception approach, dubbed MVP, for large-scale, target-driven navigation tasks.
Our MVP-based method can learn faster, and is more accurate and robust to both extreme environmental changes and poor GPS data.
We evaluate our method on two large real-world datasets, Oxford Robotcar and Nordland Railway.
arXiv Detail & Related papers (2020-03-02T05:19:52Z) - Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and
On-Device Inference [49.88536971774444]
Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.
Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services.
We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
arXiv Detail & Related papers (2020-01-13T04:41:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.