Probabilistic Grid SLAM for Low-Compute Mobile Navigation
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Solution Overview
Problem
Existing SLAM methods face challenges with high computational complexity, restrictive hypotheses, and inadequate handling of sensor measurement uncertainties, making them unsuitable for real-world applications with limited computing power, especially in navigating environments with unknown or dynamic conditions.
Innovation Solution
A method for simultaneous localization and mapping (SLAM) using a probabilistic model for distance sensors and odometry measurements, which updates occupancy and pose grids efficiently, accounting for measurement uncertainties and sensor errors, and employing integer computations to reduce computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SLAM methods (FastSLAM, scan matching) are used, then localization and mapping functionality is achieved, but computational complexity becomes too high for embedded systems with limited processing power
Solution Approach 1:
The patent segments the SLAM problem into two independent grids: an occupancy grid for environment mapping and a pose grid for localization. Each grid is updated separately using specific sensor measurements, avoiding the computational burden of traditional unified SLAM algorithms. The occupancy grid handles obstacle detection while the pose grid handles position estimation, allowing parallel and efficient computation on embedded systems.
Solution Approach 2:
The patent changes the computational parameters by using discrete probability distributions represented as histograms rather than continuous probability densities. This discretization allows the use of efficient integer arithmetic and lookup tables, significantly reducing computational complexity while maintaining the probabilistic nature of SLAM. The method uses quantized probability values and integer-based updates instead of floating-point calculations.
2Device complexity
If restrictive hypotheses are made to simplify SLAM algorithms, then computational complexity is reduced, but measurement uncertainties and sensor errors are not properly handled
Solution Approach 1:
The patent implements feedback mechanisms where sensor measurements continuously update both the occupancy grid and pose grid. The system uses feedback loops to incorporate new distance measurements and odometry data, adjusting the probability distributions in both grids. This allows the system to adapt to measurement uncertainties by continuously refining its estimates based on new sensor data, maintaining measurement precision without requiring restrictive hypotheses.
3Ease of operation
If existing SLAM methods are used in dynamic environments, then navigation capability is provided, but robustness deteriorates due to inadequate handling of sensor errors and environmental changes
Solution Approach 1:
The patent employs dynamic updates to both the occupancy grid and pose grid, allowing the system to adapt to changing environmental conditions. The occupancy grid can be updated at different rates depending on sensor availability and environmental dynamics, while the pose grid continuously integrates odometry measurements. This dynamic approach allows the system to handle moving obstacles and environmental changes robustly, maintaining navigation capability in dynamic environments without requiring restrictive assumptions about environment staticness.
Data Source
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AI summary
A method of performing simultaneous localization of a mobile body and mapping of its environment, comprising the steps of: a) defining an occupancy grid (G) on a region of said environment and a pose grid (Π) comprising for the mobile body; b) receiving a first time series of distance measurements (z1, z2) and, upon reception of a distance measurement: either b1) updating occupancy probabilities of the occupancy grid; or b2) updating pose probabilities of the pose grid; and c) receiving a second time series of odometry measurements (u1, u2), each of said measurements being representative of a motion of the mobile body in the environment and, upon reception of an odometry measurement, updating the pose probabilities of the pose grid. An apparatus for carrying out such a method.