A Combined LiDAR-Camera Localization for Autonomous Race
                    Cars

Sauerbeck, Florian; Baierlein, Lucas; Betz, Johannes; Lienkamp, Markus

doi:10.4271/12-05-01-0006

Cited by 7 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Sauerbeck et al (2022), we introduced a localization algorithm that uses camera images and LiDAR point clouds for ego pose estimation. However, real‐world testing showed no benefit over a redundant differential GNSS setup at open‐sky race tracks.…”

Section: Tum Autonomous Motorsport Softwarementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em m s − 2 $28\,ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.

show abstract

Section: Tum Autonomous Motorsport Softwarementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…An obvious approach is to build robots and run them in the same environment. That is the idea behind robotics competitions such as RoboCup [2], DARPA Grand Challenge [3], Indy Autonomous Challenge [4] and others. Unfortunately, the competitions allow us to compare algorithms only indirectly.…”

Section: Related Workmentioning

confidence: 99%

Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

Griguletskii

Shipitko²,

Abramov

et al. 2023

IEEE Access

View full text Add to dashboard Cite

A robot localization problem demands a fair comparison of the positioning algorithms. A reference trajectory of the robot's movement is needed to estimate errors and evaluate a quality of the localization. In this article, we propose the Prior Distribution Refinement method for generating a reference trajectory of a mobile robot with the Monte Carlo-based localization system. The proposed approach can be applied for both indoor and outdoor environments of an arbitrary size without the need for expensive position tracking sensors or intervention in the testing infrastructure. The reference trajectory is generated by running the algorithm over a so-called Particles' Transition Graph, obtained from a resampling stage of Monte Carlo localization. The prior distribution of particles is then refined by forward-backward propagation through the graph and exploring the connections between particles. The Viterbi algorithm is applied afterwards to generate a reference trajectory based on refined particles' distribution. We demonstrate that such an approach is capable of generating accurate estimates of a mobile robot's position and orientation with the only requirement of moderate quality of localization system being used as a core algorithm for iterative optimization.

show abstract

“…4). Here, the original localization module introduced in [46] is shown. The plan was to use a combination of LiDAR and camera with known track information and the possibility to map the track before racing.…”

Section: B Keep It Simplementioning

confidence: 99%

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

et al. 2023

Self Cite

View full text Add to dashboard Cite

The objective of this work is to provide a comprehensive understanding of the development of autonomous vehicle perception systems. So far, most autonomy perception research has been concentrated on improving perception systems' algorithmic quality or combining different sensor setups. In our work, we draw conclusions from participating in the Indy Autonomous Challenge 2021 and its follow-up event in Las Vegas 2022. These were the first head-to-head autonomous racing competitions that required an entire perception pipeline to perceive the environment and the opposing surrounding vehicles. Our research includes quantitative results from collected vehicle data and qualitative results from simulation, video, and multiple race analysis. The Indy Autonomous Challenge was one of the few research projects that considered the entire autonomous vehicle. Therefore, our findings indicate insights on the system level, including hardware setup and full-stack software. We can demonstrate that different sensor modalities in the vehicle have strengths and weaknesses when they are deployed. Our results further show the difficulties and challenges that emerge when multi-modal perception systems must run in real-time on real-world autonomous vehicles. The most concise finding from our investigation is the summary of critical learnings when developing and deploying perception systems for autonomous systems. Given the background of the study, it was inevitable that our conclusions were influenced by driving on the racetrack and only one hardware setup available. Therefore, in the discussion, we draw further parallels to driving on public roads in dense traffic. More studies are needed to investigate the development and deployment of multi-modal perception systems for autonomous road vehicles with different hardware setups and various object detection, localization, and prediction algorithms. The novel contributions of this work are given by 12 lessons learned, summarized in 5 categories. These were derived and validated through a realized real-world application project. The videos of the final events in Indianapolis and Las Vegas can be watched here: IAC: https://www.youtube.com/watch?v=ERTffn3IpIs&ab_channel=CNETHighlights AC@CES: https://www.youtube.com/watch?v=df9f4Qfa0uU&ab_channel=CNETHighlights Multiple modules of the software stack are open source: https://github.com/TUMFTM.INDEX TERMS Autonomous racing, autonomous vehicles, perception systems, software development.The associate editor coordinating the review of this manuscript and approving it for publication was Junho Hong . FIGURE 2. Main contribution of this work: the lessons learned presented.to develop, deploy, validate, and test a complete autonomous driving stack, from sensor data to vehicle actuation. Because of the challenge's tight schedule, software development had to begin before the vehicles were built. This made the simulation of the vehicles and the environment an important task to be successful in the final competition.The IAC took place on October 23, 2021, i...

show abstract

A Combined LiDAR-Camera Localization for Autonomous Race Cars

Cited by 7 publications

References 0 publications

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

Contact Info

Product

Resources

About