Low-cost Real-Time Learning-based Localization for Autonomous Vehicles

A major operational expense for an autonomous vehicle (AV) is with capturing, processing, and updating high-definition maps to localize itself when driving. To safely navigate, AVs need to know where they are on a given map to determine their trajectory to the next waypoint. Precise localization is a challenge in Global Positioning System (GPS)-denied areas such as dense urban corridors and motion tracking experiences dropouts in large open spaces such as rural highways. Classic localization algorithms are iterative, and their performance relies on direct feature matching between the stored map and the current sensor observations. This makes them prone to errors in large open spaces which have few distinguishing surface features. They are expensive to run in the vehicle as they account for a large share of the computation cost and power consumption. Better accuracy and faster localization directly improve AV safety as they navigate around people and cluttered environments. This project will develop an AV localization service that is low-cost, accurate, and can operate in real-time in any AV at a fraction of the computation and power budget of current approaches. In 2023-24 the research team developed the preliminary version of this localization approach using a specific type of neural networks (i.e. invertible neural networks) to compress the map and lookup the vehicle’s pose efficiently. The team demonstrated the accuracy and cost to operate on 1/10th-scale vehicles and benchmarked the performance using localization datasets to benchmark the performance. In 2024-25, the team will undertake the real-world evaluation on real AVs with their deployment partner, The Autoware Foundation. The team will focus on localization of an electric autonomous goods and person cart for intralogistics for indoor and outdoor navigation. The outcome of this work will result in a portable and easy-to-use localization system for Safety21 projects. Technical details: AV localization is the problem of finding a robot’s pose using a map and sensor measurements, like LiDAR scans and camera images. However, finding injective mappings between measurements and poses is difficult because sensor measurements from multiple distant poses can be similar. To solve this ambiguity, Monte Carlo Localization, the widely adopted method, uses random hypothesis sampling and sensor measurement updates to infer the pose. Other common approaches are to use Bayesian filtering or to find better distinguishable global descriptors on the map. Recent developments in localization research usually propose better measurement models or feature extractors within these frameworks. In this project, the team proposes a radically new approach to frame the localization problem as an ambiguous inverse problem and solve it with an invertible neural network (INN). The team has recently demonstrated that INNs are naturally suitable for the localization problem with many benefits, in terms of high accuracy (within 0.25m for city-scale maps), high-speed operation (>150Hz) and operate on low-cost embedded system hardware. The team will demonstrate this on point-cloud and camera datasets with evaluation on indoor and outdoor localization benchmarks, and also deploy it on real autonomous vehicles around the 23-acre Pennovation campus to show real-time and scalable operation.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/564

Language

• English

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Pennsylvania

  ,    
• Principal Investigators:

  Mangharam, Rahul

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Data quality; Location; Neural networks
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01933404
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Oct 13 2024 9:38AM