Christian Heigele

GridTiles: A method for modelling a spatial unconstrained environment

The objective of this paper is the modelling of an unbounded environment of a human-driven car that may contain multilevel structures such as bridges or parking decks. Such a model might be used by a driver assistant system (DAS) where one drives through an urban environment, requests for an assistance and the DAS should immediately be able to give the user the required support. E.g. it can guide through a narrow passage or a turn. For such an assistance an environment model is needed that runs in real-time. But to keep the system at low cost the required memory should be as small as possible. Hence the algorithm should be optimized with respect to computational power and memory consumption. A new approach is proposed that models the environment by incrementally adding small tiles at places where obstacles created measurements. Each tile contains an occupancy grid and some neighbourhood relations. By modelling the world this way, a compact representation of the environment is created that is aligned at the user-given trajectory and the allocation of obstacles. By using a grid based algorithm efficient and hence fast techniques can be used to work on the world representation. Also an extension is introduced to restrict the required memory to a given limit and concurrently map the local obstacles but avoids any transformation of historic data. A comparision with state of the art algorithms was made and the capability of the proposed algorithm is demonstrated with some experimental results.

Performance evaluation and statistical analysis of algorithms for ego-motion estimation

This contribution investigates algorithms for egomotion estimation from environmental features. Various formulations for solving the underlying procrustes problem exist. It is analytically shown that in the 2-D case this can be performed more efficiently compared to common implementations based on matrix decompositions. Furthermore, analytic error propagation is performed to second order which reveals a multiplicative estimator bias. A novel bias-corrected solution is proposed and evaluated in Monte Carlo simulations. Propagation of the derived error model to a representation used in the recursive trajectory reconstruction is presented and verified.

Effiziente Umfeldmodellierung für Fahrerassistenzsysteme im Niedergeschwindigkeitsbereich

Mit der steigenden Automatisierung von Fahrerassistenzsystemen wachsen auch die Anforderungen an die eingesetzten Verfahren zur Umfeldmodellierung. Soll sich ein Fahrzeug weitgehend selbststandig in einem vorab unbekannten und undefinierten Umfeld bewegen, so ist eine genaue Kenntnis der Aufenthaltsorte von statischen sowie dynamischen Objekten von hoher Relevanz, um Assistenzfunktionen, wie Kollisionsvermeidung oder Pfadplanung, durchfuhren zu konnen. Die Problematik der Umfeldmodellierung ist in der Robotik seit vielen Jahren ein bekanntes Problem und gut untersucht [7]. Demnach ist es bereits eine hochkomplexe Aufgabe, wenn ein autonomer Roboter in einem gut strukturierten Gebaude interagiert, die Zeit hat Messungen in ein Umfeldmodell zu integrieren und in der Lage ist, seine Bewegung einzustellen, um Messungen verwerten [5].

Multi level modeling and loop closing with GridTiles

The objective of this paper is to use GridTiles to model a complex multi level scenario and additionally use the resulting model to help solving the loop closing problem. The GridTile algorithm is a memory and computational efficent method to map an unbounded outdoor environment of a human driven car. GridTiles are a set of small occupancy grids with a neighbourhood relation in the four directions. This paper discusses four of the major problems in modelling: the local and the global mapping, multi level modelling and loop closing. For each of these problems possible solutions will be shown. Local and global maps can be directly modelled with the standard GridTile algorithm. Because the neighbourhood of the tiles is only known locally, multi level scenarios can be modelled implicitly. Additionally a loop closing solution is proposed to correct the built model afterwards in a memory efficient way. Instead of the classic approach that has to store all measurements over an unpredictable duration, the GridTiles are used as a data storage.